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Research ArticleMulticriteria Decision-Making Approach with HesitantInterval-Valued Intuitionistic Fuzzy Sets
Juan-juan Peng12 Jian-qiang Wang1 Jing Wang1 and Xiao-hong Chen1
1 School of Business Central South University Changsha 410083 China2 School of Economics and Management Hubei University of Automotive Technology Shiyan 442002 China
Correspondence should be addressed to Jian-qiang Wang jqwangqqcom
Received 24 August 2013 Accepted 24 December 2013 Published 27 March 2014
Academic Editors X-l Luo J Mula W Szeto and T Tuma
Copyright copy 2014 Juan-juan Peng et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
The definition of hesitant interval-valued intuitionistic fuzzy sets (HIVIFSs) is developed based on interval-valued intuitionisticfuzzy sets (IVIFSs) and hesitant fuzzy sets (HFSs)Then some operations on HIVIFSs are introduced in detail and their propertiesare further discussed In addition some hesitant interval-valued intuitionistic fuzzy number aggregation operators based on t-conorms and t-norms are proposed which can be used to aggregate decision-makersrsquo information inmulticriteria decision-making(MCDM) problems Some valuable proposals of these operators are studied In particular based on algebraic and Einstein t-conorms and t-norms some hesitant interval-valued intuitionistic fuzzy algebraic aggregation operators and Einstein aggregationoperators can be obtained respectively Furthermore an approach of MCDM problems based on the proposed aggregationoperators is given using hesitant interval-valued intuitionistic fuzzy information Finally an illustrative example is provided todemonstrate the applicability and effectiveness of the developed approach and the study is supported by a sensitivity analysis anda comparison analysis
1 Introduction
Since fuzzy sets were proposed by Zadeh [1] the studieson multicriteria decision-making (MCDM) problems havemade great progress Further fuzzy sets were generalized tointuitionistic fuzzy sets (IFSs) by Atanassov [2 3] where eachelement in an IFS has a membership degree and a nonmem-bership degree between 0 and 1 respectivelyThenAtanassovand Gargov [4] proposed the notion of interval-valuedintuitionistic fuzzy sets (IVIFSs) which are the extension ofIFSs where the membership degree and nonmembershipdegree of an element in an IVIFS are respectively representedby intervals in [0 1] rather than crisp values between 0and 1 In recent years many researchers have studied thetheory of IVIFSs and applied it to various fields [5ndash8] Forinstance Atanassov [9] introduced the operators of IVIFSsLee [10] proposed a method for ranking interval-valued
intuitionistic fuzzy numbers (IVIFNs) for fuzzy decision-making problems Lee [11] provided an enhanced MCDMmethod of machine design schemes under the interval-valued intuitionistic fuzzy environment Li [12] proposed aTOPSIS based nonlinear-programming method for MCDMproblems with IVIFSs Park et al [13] extended the TOPSISmethod to solve group MCDM problems in interval-valuedintuitionistic fuzzy environment in which all the preferenceinformation provided by decision-makers is presented asIVIFNs Chen et al [14] developed an approach to tacklegroup MCDM problems in the context of IVIFSs Nayagamand Sivaraman [15] introduced a method for ranking IVIFSsand compared it to other methods by means of numericalexamples Chen et al [16] presented a MCDMmethod basedon the proposed interval-valued intuitionistic fuzzy weightedaverage (IVIFWA) operator Meng et al [17] developedan induced generalized interval-valued intuitionistic fuzzy
Hindawi Publishing Corporatione Scientific World JournalVolume 2014 Article ID 868515 22 pageshttpdxdoiorg1011552014868515
2 The Scientific World Journal
hybrid Shapley averaging (GIVIFHSA) operator and appliedit to MCDM problems
Hesitant fuzzy sets (HFSs) another extension of tradi-tional fuzzy sets provide a useful reference for our studyunder hesitant fuzzy environment HFSs were first intro-duced by Torra and Narukawa [18] and they permit themembership degrees of an element to be a set of severalpossible values between 0 and 1 HFSs are highly useful inhandling the situations where people have hesitancy in pro-viding their preferences over objects in the decision-makingprocess Some aggregation operators of HFSs were studiedand applied to decision-making problems [19ndash21] Then thecorrelation coefficients of HFSs the distance measures andcorrelation measures of HFSs were discussed [22ndash24] basedon which Peng et al [25] presented a generalized hesitantfuzzy synergetic weighted distance measure Zhang and Wei[26] developed the E-VIKOR method and TOPSIS methodto solve MCDM problems with hesitant fuzzy informationZhang [27] developed a wide range of hesitant fuzzy poweraggregation operators for hesitant fuzzy information Chen etal [28] generalized the concept of HFSs to hesitant interval-valued fuzzy sets (HIVFSs) in which themembership degreesof an element to a given set are not exactly defined butdenoted by several possible interval values Wei [29] definedHIVFSs and some hesitant interval-valued fuzzy aggregationoperators Wei and Zhao [30] developed some Einsteinoperations on HIVFSs and the induced hesitant interval-valued fuzzy Einstein aggregation (HIVFEA) operators andapplied them to MCDM problems Zhu et al [31] defineddual HFSs (DHFSs) in terms of two functions that returntwo sets ofmembership degrees and nonmembership degreesrather than crisp numbers in HFSs If the idea of dual HFSsis used from a new perspective then another extension ofHFSs may be defined in terms of one function that theelement of HFSs returns a set of IFSs which are called hes-itant intuitionistic fuzzy sets (HIFSs) But decision-makersusually cannot estimate criteria values of alternatives withexact numerical values when the information is not knownpreciselyTherefore interval values in fuzzy sets can representit better than specific numbers such as interval-valued fuzzysets (IVFSs) and IVIFSs Furthermore although the theoriesof IVIFSs and HFSs have been developed and generalizedthey cannot deal with all sorts of uncertainties in differentreal problems For example when we ask the opinion of anexpert about a certain statement he or she may answer thatthe possibility that the statement is true is [01 02] and thatthe statement is false is [04 05] or the possibility that thestatement is true is [05 06] and that the statement is false is[03 05]This issue is beyond the scope of IVFSs and IVIFSsTherefore some new theories are required
So the concept of hesitant interval-valued intuitionisticfuzzy sets (HIVIFSs) is developed in this paper Comparingto the existing fuzzy sets mentioned above HIVIFSs are anew extension of HFSs which support a more flexible andsimpler approach when decision-makers provide their deci-sion information in a hesitant interval-valued intuitionisticfuzzy environment Furthermore IVIFSsHFSsHIVFSs andHIFSs are all the special cases of HIVIFSs
In this paper HFSs are extended based on IVIFSsHIVIFSs are defined and their properties and applicationsare also discussed Thus the rest of this paper is organized asfollows In Section 2 the definitions and properties of IVIFSsand HFSs are briefly reviewed In Section 3 the notion ofHIVIFSs is proposed and the operations and properties ofHIVIFSs based on 119905-conorms and 119905-norms are discussed InSection 4 some hesitant interval-valued intuitionistic fuzzynumber aggregation operators are developed and applied toMCDMproblems Section 5 gives an example to illustrate theapplication of the developedmethod Finally the conclusionsare drawn in Section 6
2 Preliminaries
In this section some basic concepts and definitions related toHIVIFSs are introduced including interval numbers IVIFSsand HFSs These will be utilized in the subsequent analysis
21 Interval Numbers and Their Operations
Definition 1 (see [32ndash34]) Let 119886 = [119886119871 119886119880] = 119909 | 119886
119871le 119909 le
119886119880 then 119886 is called an interval number In particular if 0 le
119886119871le 119909 le 119886
119880 then 119886 is reduced to a positive interval numberConsider any two interval fuzzy numbers 119886 = [119886
119871 119886119880]
and = [119887119871 119887119880] and their operations are defined as follows
(1) 119886 = hArr 119886119871= 119887119871 119886119880= 119887119880
22 IVIFSs Atanassov first proposed IFSs being enlarge-ment and development of Zadehrsquos fuzzy sets IFSs contain thedegree of nonmembership which makes it possible for us tomodel unknown informationThe definition of IVIFSs givenby Atanassov and Gargov [4] is shown as follows
Definition 2 (see [4]) Let 119863[0 1] be the set of all closedsubintervals of the interval [0 1] Let 119883 be a given set and119883 = An IVIFS in 119883 is an expression given by 119860 =
⟨119909 120583119860(119909) ]119860(119909)⟩ | 119909 isin 119883 where 120583
119860 119883 rarr 119863[0 1] ]
119860rarr
119863[0 1] with the condition 0 lt sup119909120583119860(119909) + sup
119909]119860(119909) le
1 The intervals 120583119860(119909) and ]
119860(119909) denote the degree of
belongingness and nonbelongingness of the element 119909 to theset 119860 respectively Thus for each 119909 isin 119883 120583
119860(119909) and ]
119860(119909)
are closed intervals whose lower and upper boundaries aredenoted by 120583119871
119860(119909) 120583119880
119860(119909) and ]119871
119860(119909) ]119880119860(119909) respectively and
then
119860 = ⟨119909 [120583119871
119860(119909) 120583
119880
119860(119909)] []119871
119860(119909) ]119880
119860(119909)]⟩ | 119909 isin 119883 (1)
where 0 lt 120583119880
119860(119909) + ]119880
119860(119909) le 1 120583119871
119860(119909) ge 0 ]119871
119860(119909) ge 0 For each
element 119909 the hesitancy degree can be calculated as followsΠ119860(119909) = 1 minus 120583
119860(119909) minus ]
119860(119909) = [1 minus 120583
119880
119860(119909) minus ]119880
119860(119909) 1 minus 120583
119871
119860(119909) minus
The Scientific World Journal 3
]119871119860(119909)] The set of all IVIFSs in 119883 is denoted by IVIFS(119883)
An interval-valued intuitionistic fuzzy number (IVIFN) isdenoted by 119860 = ([119886 119887] [119888 119889]) and the degree of hesitance isdenoted by [119890 119891] = [1 minus 119886 minus 119889 1 minus 119886 minus 119888] for convenience
Definition 3 (see [16]) Let 119894= ⟨[119886119894 119887119894] [119888119894 119889119894]⟩ (1 le 119894 le 119899)
be a collection of IVIFNs and let 119908119894(1 le 119894 le 119899) be the crisp
values where 119894= ⟨[119886119894 119887119894] [119888119894 119889119894]⟩ = [[119886
119894 119887119894] [1 minus 119889
119894 1 minus 119888
119894]]
0 le 119886119894le 119887119894le 1 0 le 119888
119894le 119889119894le 1 0 le 119887
119894+ 119889119894le 1 and
1 le 119894 le 119899 and then the interval-valued intuitionistic fuzzyweighted average operator can be defined as follows
IVIFWA119908(1 2
119899)
=sum119899
119894=1[[119886119894 119887119894] [1 minus 119889
[1 minus 119889 1 minus 119888]⟩ is an interval-valued intuitionistic fuzzy value119886 119888 and 119889 are calculated by the Karnik-Mendel algorithms[35]
Example 4 Let 1
= ⟨[03 06] [01 02]⟩ and 2
=
⟨[04 06] [01 03]⟩ be two IVIFNs and 1199081= 03 119908
2= 05
According to (2)
IVIFWA119908(1 2)
= [[03 times 03 + 04 times 05
03 + 0506 times 03 + 06 times 06
03 + 05]
[(1 minus 02) times 03 + (1 minus 03) times 05
03 + 05
(1 minus 01) times 03 + (1 minus 01) times 05
03 + 05]]
= [[03625 06750] [07375 09000]]
= ⟨[03625 06750] [1 minus 09000 1 minus 07375]⟩
= ⟨[03625 06750] [01000 02625]⟩
(3)
Definition 5 (see [36]) Let = ⟨[119886 119887] [119888 119889]⟩ be an IVIFNand then an accuracy function 119871() can be defined as follows
119871 () =119886 + 119887 minus 119889 (1 minus 119887) minus 119888 (1 minus 119886)
2 (4)
where 119871() isin [minus1 1] and 1 le 119894 le 119899
Definition 6 (see [36]) Let 1and 2be two IVIFNs and then
the following comparison method must exist
(1) If 119871(1) gt 119871(
2) then
1gt 2
(2) If 119871(1) = 119871(
2) then
1= 2
Example 7 Let 1= ⟨[04 06] [01 02]⟩ and
2= ⟨[05
06] [02 03]⟩ be two IVIFNs According to (4) 119871(1) =
(04 + 06 minus 02 times (1 minus 06) minus 01 times (1 minus 04))2 = 043 and119871(2) = 044 119871(
2) gt 119871(
1) can be obtained so the optimal
one(s) is 2
Definition 8 (see [37ndash39]) A function 119879 [0 1] times [0 1] rarr
[0 1] is called 119905-norm if it satisfies the following conditions
(1) for all 119909 isin [0 1] 119879(1 119909) = 119909(2) for all 119909 119910 isin [0 1] 119879(119909 119910) = 119879(119910 119909)(3) for all 119909 119910 119911 isin [0 1] 119879(119909 119879(119910 119911)) = 119879(119879(119909 119910) 119911)(4) if 119909 le 119909
1015840 119910 le 119910
1015840 then 119879(119909 119910) le 119879(1199091015840 1199101015840)
Definition 9 (see [37ndash39]) A function 119878 [0 1] times [0 1] rarr
[0 1] is called 119905-conorm if it satisfies the following conditions
(1) for all 119909 isin [0 1] 119878(0 119909) = 119909(2) for all 119909 119910 isin [0 1] 119878(119909 119910) = 119878(119910 119909)(3) for all 119909 119910 119911 isin [0 1] 119878(119909 119878(119910 119911)) = 119878(119878(119909 119910) 119911)(4) if 119909 le 119909
1015840 119910 le 119910
1015840 then 119878(119909 119910) le 119878(1199091015840 1199101015840)
There are some well-known Archimedean 119905-conorms and 119905-norms [39 40]
(1) Let 119896(119905) = minus In 119905 119897(119905) = minus In(1 minus 119905) 119896minus1(119905) = 119890minus119905
119897minus1(119905) = 1 minus 119890
minus119905 and then algebraic 119905-conorms and 119905-norms are obtained as follows119879(119909 119910) = 119909119910 119878(119909 119910) =1 minus (1 minus 119909)(1 minus 119910)
(2) Let 119896(119905) = In((2 minus 119905)119905) 119897(119905) = In((2 minus (1 minus 119905))(1 minus
119905)) 119896minus1(119905) = 2(119890119905+ 1) 119897minus1(119905) = 1 minus (2(119890
119905+ 1)) and
then Einstein 119905-conorms and 119905-norms are obtained asfollows 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) 119878(119909 119910) =(119909 + 119910)(1 + 119909119910)
(3) Let 119896(119905) = In((120574 minus (1 minus 120574)119905)119905) 120574 gt 0 119897(119905) = In((120574 minus(1 minus 120574)(1 minus 119905))(1 minus 119905)) 119896minus1(119905) = 120574(119890
119905+ 120574 minus 1) 119897minus1(119905) =
1minus(120574(119890119905+120574minus1)) and thenHamacher 119905-conorms and
119905-norms are obtained as follows
119879 (119909 119910) =119909119910
120574 + (1 minus 120574) (119909 + 119910 minus 119909119910) 120574 gt 0
119878 (119909 119910) =119909 + 119910 minus 119909119910 minus (1 minus 120574) 119909119910
1 minus (1 minus 120574) 119909119910 120574 gt 0
(5)
Based on the Archimedean 119905-conorms and 119905-normssome operations of IVIFSs are discussed as follows
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
23 HFSs
Definition 11 (see [44]) Let119883 be a universal set and aHFS on119883 is in terms of a function that when applied to119883will returna subset of [0 1] which can be represented as follows
where ℎ119864(119909) is a set of values in [0 1] denoting the possible
membership degrees of the element 119909 isin 119883 to the set 119864 ℎ119864(119909)
is called a hesitant fuzzy element (HFE) [23] and 119867 is theset of all HFEs It is noteworthy that if 119883 contains only oneelement then 119864 is called a hesitant fuzzy number (HFN)briefly denoted by 119864 = ℎ
119864(119909) The set of all hesitant fuzzy
numbers is represented as HFNSTorra [44] defined some operations on HFNs and Xia
and Xu [19 22] defined some new operations on HFNs andthe score function
Definition 12 (see [43]) Let ℎ ℎ1 and ℎ
2be three HFNs 120582 ge
0 and then four operations are defined as follows
(1) ℎ120582 = ⋃120574isinℎ
119896minus1(120582119896(120574))
(2) 120582ℎ = ⋃120574isinℎ
119897minus1(120582119897(120574))
(3) ℎ1oplus ℎ2= ⋃1205741isinℎ11205742isinℎ2
119897minus1(119897(1205741) + 119897(120574
2))
(4) ℎ1otimes ℎ2= ⋃1205741isinℎ11205742isinℎ2
119896minus1(119896(1205741) + 119896(120574
2))
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
Definition 13 (see [19]) Let ℎ isin HFNs and 119904(ℎ) =
(1ℎ)sum120574isinℎ
120574 is called the score function of ℎ where ℎ isthe number of elements in ℎ For two HFNs ℎ
1and ℎ
2 if
119904(ℎ1) gt 119904(ℎ
2) then ℎ
1gt ℎ2 if 119904(ℎ
1) = 119904(ℎ
2) then ℎ
1= ℎ2
Example 14 Let ℎ1= 03 05 06 ℎ
2= 04 07 be two
HFNs According to Definition 13 119904(ℎ1) = (13)times (03+05+
06) = 04667 119904(ℎ2) = 055 119904(ℎ
2) gt 119904(ℎ
1) so ℎ
2gt ℎ1
Furthermore Torra and Narukawa [18 44] proposed anaggregation principle for HFEs
Definition 15 (see [18 44]) Let 119864 = ℎ1 ℎ2 ℎ
119899 be a set of
119899 HFEs let 120599 be a function on 119864 and let 120599 [0 1]119899 rarr [0 1]and then
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 In some cases decision-makers
usually cannot estimate criteria values of alternatives with anexact numerical value when the information is not preciselyknown Therefore interval values in fuzzy sets can representit better than specific numbers such as IVFSs and IVIFSsFurthermore IVIFSs could describe the object being ldquoneitherthis nor thatrdquo and the membership degree and nonmember-ship degree of IVIFSs are interval values respectively Thusprecise numerical values in HFSs can be replaced by IVIFSswhich are more flexible in the real world and this is what thissection will solve
Definition 16 Assume that 119883 is a finite universal set AHIVIFS 119860 in119883 is an object in the following form
where 119867119864(119909) is a finite set of values in IVIFSs denoting the
possiblemembership degrees andnonmembership degrees ofthe element 119909 isin 119883 to the set 119864
Based on the definition given above
119867119864 (119909) =
119899(119867119864(119909))
⋃119894=1
⟨[120583119871
119864119894
(119909) 120583119880
119864119894
(119909)] []119871119864119894
(119909) ]119880119864119894
(119909)]⟩
(9)
where 0 le 120583119871
1198641
(119909) le 120583119880
1198641
(119909) le 120583119871
1198642
(119909) le 120583119880
1198642
(119909) le sdot sdot sdot
120583119871
119899(119867119864(119909))
(119909) le 120583119880
119899(119867119864(119909))
(119909) le 1 0 le 120583119880
119864119894
(119909) + ]119880119864119894
(119909) le 1120583119871
119864119894
(119909) ge 0 ]119871119864119894
(119909) ge 0 and 119899(119867119864(119909)) ge 1 Actually HIVIFSs
have several possible membership degrees taking the formof IVIFSs instead of FSs in HFSs If 119899(119867
119864(119909)) = 1 then
the HIVIFS is reduced to an IVIFS if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) = ]119880119864119894
(119909) = 0 (119894 = 1 2
119899(119867119864(119909))) then the HIVIFS is reduced to a HFS if 120583119871
119864119894
(119909) =
120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) or ]119871
119864119894
(119909) = ]119880119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) then the HIVIFS is reduced to a HIVFS
if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) =
]119880119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) then the HIVIFS is reduced
to a HIFS Furthermore 119867119864(119909) is called a hesitant interval-
valued intuitionistic fuzzy element (HIVIFE) and 119864 is theset of all HIVIFEs In particular if 119883 has only one element⟨119909119867119864(119909)⟩ is called a hesitant interval-valued intuitionistic
Based on Definitions 5 6 and 13 the ranking method forHIVIFNs is defined as follows
Definition 22 Let 119867 isin HIVIFNs 119878(119867) = (1119867)sum120574isin119867
120574 iscalled the score function of 119867 where 119867 is the number ofthe interval-valued intuitionistic fuzzy values in 119867 For twoHIVIFNs 119867
1and 119867
2 if 119878(119867
1) gt 119878(119867
2) then 119867
1gt 1198672 if
119878(1198671) = 119878(119867
2) then119867
1= 1198672
Note that 119878(1198671) and 119878(119867
2) could be compared by utilizing
Definitions 5 and 6
Example 23 Let1198671= ⟨[03 04] [01 02]⟩ ⟨[03 05] [02
04]⟩ and1198672= ⟨[03 04] [02 03]⟩ be two HIVIFNs and
then
119878 (1198671) =
1
2times ⟨[03 + 03 04 + 05] [01 + 02 02 + 04]⟩
= ⟨[030 045] [015 030]⟩
119878 (1198672) = ⟨[03 04] [02 03]⟩
(12)According to Definitions 5 and 6
119871 (119878 (1198671))
=030 + 045 minus 030 times (1 minus 045) minus 015 times (1 minus 030)
2
= 024
119871 (119878 (1198672))
=03 + 04 minus 03 times (1 minus 04) minus 02 times (1 minus 03)
2
= 019
(13)
Hence 119878(1198671) gt 119878(119867
2) which indicates that 119867
1is preferred
to1198672
4 HIVIFN Aggregation Operators and TheirApplications in MCDM Problems
In this section HIVIFN aggregation operators are proposedand some properties of these operators are discussed Inparticular some hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators are proposed based on alge-braic 119905-conorms and 119905-norms Then how to utilize theseoperators to MCDM problems is discussed as well
41 HIVIFN Aggregation Operators
Definition 24 Let 119867119895(119895 = 1 2 119899) be a collection of
HIVIFNs and HIVIFNWA HIVIFNS119899 rarr HIVIFNS andthen
HIVIFNWA119908(1198671 1198672 119867
119899) =
119899
⨁119895=1
119908119895119867119895 (14)
The HIVIFNWA operator is called the HIVIFN weightedaveraging operator of dimension 119899 where 119908 = (119908
1 1199082
119908119899) is the weight vector of 119867
119895(119895 = 1 2 119899) with 119908
119895ge
0 (119895 = 1 2 119899) and sum119899119895=1
119908119895= 1
Theorem 25 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs and let119908 = (1199081 1199082 119908
119899)
be the weight vector of 119867119895(119895 = 1 2 119899) with 119908
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
hybrid Shapley averaging (GIVIFHSA) operator and appliedit to MCDM problems
Hesitant fuzzy sets (HFSs) another extension of tradi-tional fuzzy sets provide a useful reference for our studyunder hesitant fuzzy environment HFSs were first intro-duced by Torra and Narukawa [18] and they permit themembership degrees of an element to be a set of severalpossible values between 0 and 1 HFSs are highly useful inhandling the situations where people have hesitancy in pro-viding their preferences over objects in the decision-makingprocess Some aggregation operators of HFSs were studiedand applied to decision-making problems [19ndash21] Then thecorrelation coefficients of HFSs the distance measures andcorrelation measures of HFSs were discussed [22ndash24] basedon which Peng et al [25] presented a generalized hesitantfuzzy synergetic weighted distance measure Zhang and Wei[26] developed the E-VIKOR method and TOPSIS methodto solve MCDM problems with hesitant fuzzy informationZhang [27] developed a wide range of hesitant fuzzy poweraggregation operators for hesitant fuzzy information Chen etal [28] generalized the concept of HFSs to hesitant interval-valued fuzzy sets (HIVFSs) in which themembership degreesof an element to a given set are not exactly defined butdenoted by several possible interval values Wei [29] definedHIVFSs and some hesitant interval-valued fuzzy aggregationoperators Wei and Zhao [30] developed some Einsteinoperations on HIVFSs and the induced hesitant interval-valued fuzzy Einstein aggregation (HIVFEA) operators andapplied them to MCDM problems Zhu et al [31] defineddual HFSs (DHFSs) in terms of two functions that returntwo sets ofmembership degrees and nonmembership degreesrather than crisp numbers in HFSs If the idea of dual HFSsis used from a new perspective then another extension ofHFSs may be defined in terms of one function that theelement of HFSs returns a set of IFSs which are called hes-itant intuitionistic fuzzy sets (HIFSs) But decision-makersusually cannot estimate criteria values of alternatives withexact numerical values when the information is not knownpreciselyTherefore interval values in fuzzy sets can representit better than specific numbers such as interval-valued fuzzysets (IVFSs) and IVIFSs Furthermore although the theoriesof IVIFSs and HFSs have been developed and generalizedthey cannot deal with all sorts of uncertainties in differentreal problems For example when we ask the opinion of anexpert about a certain statement he or she may answer thatthe possibility that the statement is true is [01 02] and thatthe statement is false is [04 05] or the possibility that thestatement is true is [05 06] and that the statement is false is[03 05]This issue is beyond the scope of IVFSs and IVIFSsTherefore some new theories are required
So the concept of hesitant interval-valued intuitionisticfuzzy sets (HIVIFSs) is developed in this paper Comparingto the existing fuzzy sets mentioned above HIVIFSs are anew extension of HFSs which support a more flexible andsimpler approach when decision-makers provide their deci-sion information in a hesitant interval-valued intuitionisticfuzzy environment Furthermore IVIFSsHFSsHIVFSs andHIFSs are all the special cases of HIVIFSs
In this paper HFSs are extended based on IVIFSsHIVIFSs are defined and their properties and applicationsare also discussed Thus the rest of this paper is organized asfollows In Section 2 the definitions and properties of IVIFSsand HFSs are briefly reviewed In Section 3 the notion ofHIVIFSs is proposed and the operations and properties ofHIVIFSs based on 119905-conorms and 119905-norms are discussed InSection 4 some hesitant interval-valued intuitionistic fuzzynumber aggregation operators are developed and applied toMCDMproblems Section 5 gives an example to illustrate theapplication of the developedmethod Finally the conclusionsare drawn in Section 6
2 Preliminaries
In this section some basic concepts and definitions related toHIVIFSs are introduced including interval numbers IVIFSsand HFSs These will be utilized in the subsequent analysis
21 Interval Numbers and Their Operations
Definition 1 (see [32ndash34]) Let 119886 = [119886119871 119886119880] = 119909 | 119886
119871le 119909 le
119886119880 then 119886 is called an interval number In particular if 0 le
119886119871le 119909 le 119886
119880 then 119886 is reduced to a positive interval numberConsider any two interval fuzzy numbers 119886 = [119886
119871 119886119880]
and = [119887119871 119887119880] and their operations are defined as follows
(1) 119886 = hArr 119886119871= 119887119871 119886119880= 119887119880
22 IVIFSs Atanassov first proposed IFSs being enlarge-ment and development of Zadehrsquos fuzzy sets IFSs contain thedegree of nonmembership which makes it possible for us tomodel unknown informationThe definition of IVIFSs givenby Atanassov and Gargov [4] is shown as follows
Definition 2 (see [4]) Let 119863[0 1] be the set of all closedsubintervals of the interval [0 1] Let 119883 be a given set and119883 = An IVIFS in 119883 is an expression given by 119860 =
⟨119909 120583119860(119909) ]119860(119909)⟩ | 119909 isin 119883 where 120583
119860 119883 rarr 119863[0 1] ]
119860rarr
119863[0 1] with the condition 0 lt sup119909120583119860(119909) + sup
119909]119860(119909) le
1 The intervals 120583119860(119909) and ]
119860(119909) denote the degree of
belongingness and nonbelongingness of the element 119909 to theset 119860 respectively Thus for each 119909 isin 119883 120583
119860(119909) and ]
119860(119909)
are closed intervals whose lower and upper boundaries aredenoted by 120583119871
119860(119909) 120583119880
119860(119909) and ]119871
119860(119909) ]119880119860(119909) respectively and
then
119860 = ⟨119909 [120583119871
119860(119909) 120583
119880
119860(119909)] []119871
119860(119909) ]119880
119860(119909)]⟩ | 119909 isin 119883 (1)
where 0 lt 120583119880
119860(119909) + ]119880
119860(119909) le 1 120583119871
119860(119909) ge 0 ]119871
119860(119909) ge 0 For each
element 119909 the hesitancy degree can be calculated as followsΠ119860(119909) = 1 minus 120583
119860(119909) minus ]
119860(119909) = [1 minus 120583
119880
119860(119909) minus ]119880
119860(119909) 1 minus 120583
119871
119860(119909) minus
The Scientific World Journal 3
]119871119860(119909)] The set of all IVIFSs in 119883 is denoted by IVIFS(119883)
An interval-valued intuitionistic fuzzy number (IVIFN) isdenoted by 119860 = ([119886 119887] [119888 119889]) and the degree of hesitance isdenoted by [119890 119891] = [1 minus 119886 minus 119889 1 minus 119886 minus 119888] for convenience
Definition 3 (see [16]) Let 119894= ⟨[119886119894 119887119894] [119888119894 119889119894]⟩ (1 le 119894 le 119899)
be a collection of IVIFNs and let 119908119894(1 le 119894 le 119899) be the crisp
values where 119894= ⟨[119886119894 119887119894] [119888119894 119889119894]⟩ = [[119886
119894 119887119894] [1 minus 119889
119894 1 minus 119888
119894]]
0 le 119886119894le 119887119894le 1 0 le 119888
119894le 119889119894le 1 0 le 119887
119894+ 119889119894le 1 and
1 le 119894 le 119899 and then the interval-valued intuitionistic fuzzyweighted average operator can be defined as follows
IVIFWA119908(1 2
119899)
=sum119899
119894=1[[119886119894 119887119894] [1 minus 119889
[1 minus 119889 1 minus 119888]⟩ is an interval-valued intuitionistic fuzzy value119886 119888 and 119889 are calculated by the Karnik-Mendel algorithms[35]
Example 4 Let 1
= ⟨[03 06] [01 02]⟩ and 2
=
⟨[04 06] [01 03]⟩ be two IVIFNs and 1199081= 03 119908
2= 05
According to (2)
IVIFWA119908(1 2)
= [[03 times 03 + 04 times 05
03 + 0506 times 03 + 06 times 06
03 + 05]
[(1 minus 02) times 03 + (1 minus 03) times 05
03 + 05
(1 minus 01) times 03 + (1 minus 01) times 05
03 + 05]]
= [[03625 06750] [07375 09000]]
= ⟨[03625 06750] [1 minus 09000 1 minus 07375]⟩
= ⟨[03625 06750] [01000 02625]⟩
(3)
Definition 5 (see [36]) Let = ⟨[119886 119887] [119888 119889]⟩ be an IVIFNand then an accuracy function 119871() can be defined as follows
119871 () =119886 + 119887 minus 119889 (1 minus 119887) minus 119888 (1 minus 119886)
2 (4)
where 119871() isin [minus1 1] and 1 le 119894 le 119899
Definition 6 (see [36]) Let 1and 2be two IVIFNs and then
the following comparison method must exist
(1) If 119871(1) gt 119871(
2) then
1gt 2
(2) If 119871(1) = 119871(
2) then
1= 2
Example 7 Let 1= ⟨[04 06] [01 02]⟩ and
2= ⟨[05
06] [02 03]⟩ be two IVIFNs According to (4) 119871(1) =
(04 + 06 minus 02 times (1 minus 06) minus 01 times (1 minus 04))2 = 043 and119871(2) = 044 119871(
2) gt 119871(
1) can be obtained so the optimal
one(s) is 2
Definition 8 (see [37ndash39]) A function 119879 [0 1] times [0 1] rarr
[0 1] is called 119905-norm if it satisfies the following conditions
(1) for all 119909 isin [0 1] 119879(1 119909) = 119909(2) for all 119909 119910 isin [0 1] 119879(119909 119910) = 119879(119910 119909)(3) for all 119909 119910 119911 isin [0 1] 119879(119909 119879(119910 119911)) = 119879(119879(119909 119910) 119911)(4) if 119909 le 119909
1015840 119910 le 119910
1015840 then 119879(119909 119910) le 119879(1199091015840 1199101015840)
Definition 9 (see [37ndash39]) A function 119878 [0 1] times [0 1] rarr
[0 1] is called 119905-conorm if it satisfies the following conditions
(1) for all 119909 isin [0 1] 119878(0 119909) = 119909(2) for all 119909 119910 isin [0 1] 119878(119909 119910) = 119878(119910 119909)(3) for all 119909 119910 119911 isin [0 1] 119878(119909 119878(119910 119911)) = 119878(119878(119909 119910) 119911)(4) if 119909 le 119909
1015840 119910 le 119910
1015840 then 119878(119909 119910) le 119878(1199091015840 1199101015840)
There are some well-known Archimedean 119905-conorms and 119905-norms [39 40]
(1) Let 119896(119905) = minus In 119905 119897(119905) = minus In(1 minus 119905) 119896minus1(119905) = 119890minus119905
119897minus1(119905) = 1 minus 119890
minus119905 and then algebraic 119905-conorms and 119905-norms are obtained as follows119879(119909 119910) = 119909119910 119878(119909 119910) =1 minus (1 minus 119909)(1 minus 119910)
(2) Let 119896(119905) = In((2 minus 119905)119905) 119897(119905) = In((2 minus (1 minus 119905))(1 minus
119905)) 119896minus1(119905) = 2(119890119905+ 1) 119897minus1(119905) = 1 minus (2(119890
119905+ 1)) and
then Einstein 119905-conorms and 119905-norms are obtained asfollows 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) 119878(119909 119910) =(119909 + 119910)(1 + 119909119910)
(3) Let 119896(119905) = In((120574 minus (1 minus 120574)119905)119905) 120574 gt 0 119897(119905) = In((120574 minus(1 minus 120574)(1 minus 119905))(1 minus 119905)) 119896minus1(119905) = 120574(119890
119905+ 120574 minus 1) 119897minus1(119905) =
1minus(120574(119890119905+120574minus1)) and thenHamacher 119905-conorms and
119905-norms are obtained as follows
119879 (119909 119910) =119909119910
120574 + (1 minus 120574) (119909 + 119910 minus 119909119910) 120574 gt 0
119878 (119909 119910) =119909 + 119910 minus 119909119910 minus (1 minus 120574) 119909119910
1 minus (1 minus 120574) 119909119910 120574 gt 0
(5)
Based on the Archimedean 119905-conorms and 119905-normssome operations of IVIFSs are discussed as follows
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
23 HFSs
Definition 11 (see [44]) Let119883 be a universal set and aHFS on119883 is in terms of a function that when applied to119883will returna subset of [0 1] which can be represented as follows
where ℎ119864(119909) is a set of values in [0 1] denoting the possible
membership degrees of the element 119909 isin 119883 to the set 119864 ℎ119864(119909)
is called a hesitant fuzzy element (HFE) [23] and 119867 is theset of all HFEs It is noteworthy that if 119883 contains only oneelement then 119864 is called a hesitant fuzzy number (HFN)briefly denoted by 119864 = ℎ
119864(119909) The set of all hesitant fuzzy
numbers is represented as HFNSTorra [44] defined some operations on HFNs and Xia
and Xu [19 22] defined some new operations on HFNs andthe score function
Definition 12 (see [43]) Let ℎ ℎ1 and ℎ
2be three HFNs 120582 ge
0 and then four operations are defined as follows
(1) ℎ120582 = ⋃120574isinℎ
119896minus1(120582119896(120574))
(2) 120582ℎ = ⋃120574isinℎ
119897minus1(120582119897(120574))
(3) ℎ1oplus ℎ2= ⋃1205741isinℎ11205742isinℎ2
119897minus1(119897(1205741) + 119897(120574
2))
(4) ℎ1otimes ℎ2= ⋃1205741isinℎ11205742isinℎ2
119896minus1(119896(1205741) + 119896(120574
2))
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
Definition 13 (see [19]) Let ℎ isin HFNs and 119904(ℎ) =
(1ℎ)sum120574isinℎ
120574 is called the score function of ℎ where ℎ isthe number of elements in ℎ For two HFNs ℎ
1and ℎ
2 if
119904(ℎ1) gt 119904(ℎ
2) then ℎ
1gt ℎ2 if 119904(ℎ
1) = 119904(ℎ
2) then ℎ
1= ℎ2
Example 14 Let ℎ1= 03 05 06 ℎ
2= 04 07 be two
HFNs According to Definition 13 119904(ℎ1) = (13)times (03+05+
06) = 04667 119904(ℎ2) = 055 119904(ℎ
2) gt 119904(ℎ
1) so ℎ
2gt ℎ1
Furthermore Torra and Narukawa [18 44] proposed anaggregation principle for HFEs
Definition 15 (see [18 44]) Let 119864 = ℎ1 ℎ2 ℎ
119899 be a set of
119899 HFEs let 120599 be a function on 119864 and let 120599 [0 1]119899 rarr [0 1]and then
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 In some cases decision-makers
usually cannot estimate criteria values of alternatives with anexact numerical value when the information is not preciselyknown Therefore interval values in fuzzy sets can representit better than specific numbers such as IVFSs and IVIFSsFurthermore IVIFSs could describe the object being ldquoneitherthis nor thatrdquo and the membership degree and nonmember-ship degree of IVIFSs are interval values respectively Thusprecise numerical values in HFSs can be replaced by IVIFSswhich are more flexible in the real world and this is what thissection will solve
Definition 16 Assume that 119883 is a finite universal set AHIVIFS 119860 in119883 is an object in the following form
where 119867119864(119909) is a finite set of values in IVIFSs denoting the
possiblemembership degrees andnonmembership degrees ofthe element 119909 isin 119883 to the set 119864
Based on the definition given above
119867119864 (119909) =
119899(119867119864(119909))
⋃119894=1
⟨[120583119871
119864119894
(119909) 120583119880
119864119894
(119909)] []119871119864119894
(119909) ]119880119864119894
(119909)]⟩
(9)
where 0 le 120583119871
1198641
(119909) le 120583119880
1198641
(119909) le 120583119871
1198642
(119909) le 120583119880
1198642
(119909) le sdot sdot sdot
120583119871
119899(119867119864(119909))
(119909) le 120583119880
119899(119867119864(119909))
(119909) le 1 0 le 120583119880
119864119894
(119909) + ]119880119864119894
(119909) le 1120583119871
119864119894
(119909) ge 0 ]119871119864119894
(119909) ge 0 and 119899(119867119864(119909)) ge 1 Actually HIVIFSs
have several possible membership degrees taking the formof IVIFSs instead of FSs in HFSs If 119899(119867
119864(119909)) = 1 then
the HIVIFS is reduced to an IVIFS if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) = ]119880119864119894
(119909) = 0 (119894 = 1 2
119899(119867119864(119909))) then the HIVIFS is reduced to a HFS if 120583119871
119864119894
(119909) =
120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) or ]119871
119864119894
(119909) = ]119880119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) then the HIVIFS is reduced to a HIVFS
if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) =
]119880119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) then the HIVIFS is reduced
to a HIFS Furthermore 119867119864(119909) is called a hesitant interval-
valued intuitionistic fuzzy element (HIVIFE) and 119864 is theset of all HIVIFEs In particular if 119883 has only one element⟨119909119867119864(119909)⟩ is called a hesitant interval-valued intuitionistic
Based on Definitions 5 6 and 13 the ranking method forHIVIFNs is defined as follows
Definition 22 Let 119867 isin HIVIFNs 119878(119867) = (1119867)sum120574isin119867
120574 iscalled the score function of 119867 where 119867 is the number ofthe interval-valued intuitionistic fuzzy values in 119867 For twoHIVIFNs 119867
1and 119867
2 if 119878(119867
1) gt 119878(119867
2) then 119867
1gt 1198672 if
119878(1198671) = 119878(119867
2) then119867
1= 1198672
Note that 119878(1198671) and 119878(119867
2) could be compared by utilizing
Definitions 5 and 6
Example 23 Let1198671= ⟨[03 04] [01 02]⟩ ⟨[03 05] [02
04]⟩ and1198672= ⟨[03 04] [02 03]⟩ be two HIVIFNs and
then
119878 (1198671) =
1
2times ⟨[03 + 03 04 + 05] [01 + 02 02 + 04]⟩
= ⟨[030 045] [015 030]⟩
119878 (1198672) = ⟨[03 04] [02 03]⟩
(12)According to Definitions 5 and 6
119871 (119878 (1198671))
=030 + 045 minus 030 times (1 minus 045) minus 015 times (1 minus 030)
2
= 024
119871 (119878 (1198672))
=03 + 04 minus 03 times (1 minus 04) minus 02 times (1 minus 03)
2
= 019
(13)
Hence 119878(1198671) gt 119878(119867
2) which indicates that 119867
1is preferred
to1198672
4 HIVIFN Aggregation Operators and TheirApplications in MCDM Problems
In this section HIVIFN aggregation operators are proposedand some properties of these operators are discussed Inparticular some hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators are proposed based on alge-braic 119905-conorms and 119905-norms Then how to utilize theseoperators to MCDM problems is discussed as well
41 HIVIFN Aggregation Operators
Definition 24 Let 119867119895(119895 = 1 2 119899) be a collection of
HIVIFNs and HIVIFNWA HIVIFNS119899 rarr HIVIFNS andthen
HIVIFNWA119908(1198671 1198672 119867
119899) =
119899
⨁119895=1
119908119895119867119895 (14)
The HIVIFNWA operator is called the HIVIFN weightedaveraging operator of dimension 119899 where 119908 = (119908
1 1199082
119908119899) is the weight vector of 119867
119895(119895 = 1 2 119899) with 119908
119895ge
0 (119895 = 1 2 119899) and sum119899119895=1
119908119895= 1
Theorem 25 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs and let119908 = (1199081 1199082 119908
119899)
be the weight vector of 119867119895(119895 = 1 2 119899) with 119908
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
]119871119860(119909)] The set of all IVIFSs in 119883 is denoted by IVIFS(119883)
An interval-valued intuitionistic fuzzy number (IVIFN) isdenoted by 119860 = ([119886 119887] [119888 119889]) and the degree of hesitance isdenoted by [119890 119891] = [1 minus 119886 minus 119889 1 minus 119886 minus 119888] for convenience
Definition 3 (see [16]) Let 119894= ⟨[119886119894 119887119894] [119888119894 119889119894]⟩ (1 le 119894 le 119899)
be a collection of IVIFNs and let 119908119894(1 le 119894 le 119899) be the crisp
values where 119894= ⟨[119886119894 119887119894] [119888119894 119889119894]⟩ = [[119886
119894 119887119894] [1 minus 119889
119894 1 minus 119888
119894]]
0 le 119886119894le 119887119894le 1 0 le 119888
119894le 119889119894le 1 0 le 119887
119894+ 119889119894le 1 and
1 le 119894 le 119899 and then the interval-valued intuitionistic fuzzyweighted average operator can be defined as follows
IVIFWA119908(1 2
119899)
=sum119899
119894=1[[119886119894 119887119894] [1 minus 119889
[1 minus 119889 1 minus 119888]⟩ is an interval-valued intuitionistic fuzzy value119886 119888 and 119889 are calculated by the Karnik-Mendel algorithms[35]
Example 4 Let 1
= ⟨[03 06] [01 02]⟩ and 2
=
⟨[04 06] [01 03]⟩ be two IVIFNs and 1199081= 03 119908
2= 05
According to (2)
IVIFWA119908(1 2)
= [[03 times 03 + 04 times 05
03 + 0506 times 03 + 06 times 06
03 + 05]
[(1 minus 02) times 03 + (1 minus 03) times 05
03 + 05
(1 minus 01) times 03 + (1 minus 01) times 05
03 + 05]]
= [[03625 06750] [07375 09000]]
= ⟨[03625 06750] [1 minus 09000 1 minus 07375]⟩
= ⟨[03625 06750] [01000 02625]⟩
(3)
Definition 5 (see [36]) Let = ⟨[119886 119887] [119888 119889]⟩ be an IVIFNand then an accuracy function 119871() can be defined as follows
119871 () =119886 + 119887 minus 119889 (1 minus 119887) minus 119888 (1 minus 119886)
2 (4)
where 119871() isin [minus1 1] and 1 le 119894 le 119899
Definition 6 (see [36]) Let 1and 2be two IVIFNs and then
the following comparison method must exist
(1) If 119871(1) gt 119871(
2) then
1gt 2
(2) If 119871(1) = 119871(
2) then
1= 2
Example 7 Let 1= ⟨[04 06] [01 02]⟩ and
2= ⟨[05
06] [02 03]⟩ be two IVIFNs According to (4) 119871(1) =
(04 + 06 minus 02 times (1 minus 06) minus 01 times (1 minus 04))2 = 043 and119871(2) = 044 119871(
2) gt 119871(
1) can be obtained so the optimal
one(s) is 2
Definition 8 (see [37ndash39]) A function 119879 [0 1] times [0 1] rarr
[0 1] is called 119905-norm if it satisfies the following conditions
(1) for all 119909 isin [0 1] 119879(1 119909) = 119909(2) for all 119909 119910 isin [0 1] 119879(119909 119910) = 119879(119910 119909)(3) for all 119909 119910 119911 isin [0 1] 119879(119909 119879(119910 119911)) = 119879(119879(119909 119910) 119911)(4) if 119909 le 119909
1015840 119910 le 119910
1015840 then 119879(119909 119910) le 119879(1199091015840 1199101015840)
Definition 9 (see [37ndash39]) A function 119878 [0 1] times [0 1] rarr
[0 1] is called 119905-conorm if it satisfies the following conditions
(1) for all 119909 isin [0 1] 119878(0 119909) = 119909(2) for all 119909 119910 isin [0 1] 119878(119909 119910) = 119878(119910 119909)(3) for all 119909 119910 119911 isin [0 1] 119878(119909 119878(119910 119911)) = 119878(119878(119909 119910) 119911)(4) if 119909 le 119909
1015840 119910 le 119910
1015840 then 119878(119909 119910) le 119878(1199091015840 1199101015840)
There are some well-known Archimedean 119905-conorms and 119905-norms [39 40]
(1) Let 119896(119905) = minus In 119905 119897(119905) = minus In(1 minus 119905) 119896minus1(119905) = 119890minus119905
119897minus1(119905) = 1 minus 119890
minus119905 and then algebraic 119905-conorms and 119905-norms are obtained as follows119879(119909 119910) = 119909119910 119878(119909 119910) =1 minus (1 minus 119909)(1 minus 119910)
(2) Let 119896(119905) = In((2 minus 119905)119905) 119897(119905) = In((2 minus (1 minus 119905))(1 minus
119905)) 119896minus1(119905) = 2(119890119905+ 1) 119897minus1(119905) = 1 minus (2(119890
119905+ 1)) and
then Einstein 119905-conorms and 119905-norms are obtained asfollows 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) 119878(119909 119910) =(119909 + 119910)(1 + 119909119910)
(3) Let 119896(119905) = In((120574 minus (1 minus 120574)119905)119905) 120574 gt 0 119897(119905) = In((120574 minus(1 minus 120574)(1 minus 119905))(1 minus 119905)) 119896minus1(119905) = 120574(119890
119905+ 120574 minus 1) 119897minus1(119905) =
1minus(120574(119890119905+120574minus1)) and thenHamacher 119905-conorms and
119905-norms are obtained as follows
119879 (119909 119910) =119909119910
120574 + (1 minus 120574) (119909 + 119910 minus 119909119910) 120574 gt 0
119878 (119909 119910) =119909 + 119910 minus 119909119910 minus (1 minus 120574) 119909119910
1 minus (1 minus 120574) 119909119910 120574 gt 0
(5)
Based on the Archimedean 119905-conorms and 119905-normssome operations of IVIFSs are discussed as follows
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
23 HFSs
Definition 11 (see [44]) Let119883 be a universal set and aHFS on119883 is in terms of a function that when applied to119883will returna subset of [0 1] which can be represented as follows
where ℎ119864(119909) is a set of values in [0 1] denoting the possible
membership degrees of the element 119909 isin 119883 to the set 119864 ℎ119864(119909)
is called a hesitant fuzzy element (HFE) [23] and 119867 is theset of all HFEs It is noteworthy that if 119883 contains only oneelement then 119864 is called a hesitant fuzzy number (HFN)briefly denoted by 119864 = ℎ
119864(119909) The set of all hesitant fuzzy
numbers is represented as HFNSTorra [44] defined some operations on HFNs and Xia
and Xu [19 22] defined some new operations on HFNs andthe score function
Definition 12 (see [43]) Let ℎ ℎ1 and ℎ
2be three HFNs 120582 ge
0 and then four operations are defined as follows
(1) ℎ120582 = ⋃120574isinℎ
119896minus1(120582119896(120574))
(2) 120582ℎ = ⋃120574isinℎ
119897minus1(120582119897(120574))
(3) ℎ1oplus ℎ2= ⋃1205741isinℎ11205742isinℎ2
119897minus1(119897(1205741) + 119897(120574
2))
(4) ℎ1otimes ℎ2= ⋃1205741isinℎ11205742isinℎ2
119896minus1(119896(1205741) + 119896(120574
2))
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
Definition 13 (see [19]) Let ℎ isin HFNs and 119904(ℎ) =
(1ℎ)sum120574isinℎ
120574 is called the score function of ℎ where ℎ isthe number of elements in ℎ For two HFNs ℎ
1and ℎ
2 if
119904(ℎ1) gt 119904(ℎ
2) then ℎ
1gt ℎ2 if 119904(ℎ
1) = 119904(ℎ
2) then ℎ
1= ℎ2
Example 14 Let ℎ1= 03 05 06 ℎ
2= 04 07 be two
HFNs According to Definition 13 119904(ℎ1) = (13)times (03+05+
06) = 04667 119904(ℎ2) = 055 119904(ℎ
2) gt 119904(ℎ
1) so ℎ
2gt ℎ1
Furthermore Torra and Narukawa [18 44] proposed anaggregation principle for HFEs
Definition 15 (see [18 44]) Let 119864 = ℎ1 ℎ2 ℎ
119899 be a set of
119899 HFEs let 120599 be a function on 119864 and let 120599 [0 1]119899 rarr [0 1]and then
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 In some cases decision-makers
usually cannot estimate criteria values of alternatives with anexact numerical value when the information is not preciselyknown Therefore interval values in fuzzy sets can representit better than specific numbers such as IVFSs and IVIFSsFurthermore IVIFSs could describe the object being ldquoneitherthis nor thatrdquo and the membership degree and nonmember-ship degree of IVIFSs are interval values respectively Thusprecise numerical values in HFSs can be replaced by IVIFSswhich are more flexible in the real world and this is what thissection will solve
Definition 16 Assume that 119883 is a finite universal set AHIVIFS 119860 in119883 is an object in the following form
where 119867119864(119909) is a finite set of values in IVIFSs denoting the
possiblemembership degrees andnonmembership degrees ofthe element 119909 isin 119883 to the set 119864
Based on the definition given above
119867119864 (119909) =
119899(119867119864(119909))
⋃119894=1
⟨[120583119871
119864119894
(119909) 120583119880
119864119894
(119909)] []119871119864119894
(119909) ]119880119864119894
(119909)]⟩
(9)
where 0 le 120583119871
1198641
(119909) le 120583119880
1198641
(119909) le 120583119871
1198642
(119909) le 120583119880
1198642
(119909) le sdot sdot sdot
120583119871
119899(119867119864(119909))
(119909) le 120583119880
119899(119867119864(119909))
(119909) le 1 0 le 120583119880
119864119894
(119909) + ]119880119864119894
(119909) le 1120583119871
119864119894
(119909) ge 0 ]119871119864119894
(119909) ge 0 and 119899(119867119864(119909)) ge 1 Actually HIVIFSs
have several possible membership degrees taking the formof IVIFSs instead of FSs in HFSs If 119899(119867
119864(119909)) = 1 then
the HIVIFS is reduced to an IVIFS if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) = ]119880119864119894
(119909) = 0 (119894 = 1 2
119899(119867119864(119909))) then the HIVIFS is reduced to a HFS if 120583119871
119864119894
(119909) =
120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) or ]119871
119864119894
(119909) = ]119880119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) then the HIVIFS is reduced to a HIVFS
if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) =
]119880119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) then the HIVIFS is reduced
to a HIFS Furthermore 119867119864(119909) is called a hesitant interval-
valued intuitionistic fuzzy element (HIVIFE) and 119864 is theset of all HIVIFEs In particular if 119883 has only one element⟨119909119867119864(119909)⟩ is called a hesitant interval-valued intuitionistic
Based on Definitions 5 6 and 13 the ranking method forHIVIFNs is defined as follows
Definition 22 Let 119867 isin HIVIFNs 119878(119867) = (1119867)sum120574isin119867
120574 iscalled the score function of 119867 where 119867 is the number ofthe interval-valued intuitionistic fuzzy values in 119867 For twoHIVIFNs 119867
1and 119867
2 if 119878(119867
1) gt 119878(119867
2) then 119867
1gt 1198672 if
119878(1198671) = 119878(119867
2) then119867
1= 1198672
Note that 119878(1198671) and 119878(119867
2) could be compared by utilizing
Definitions 5 and 6
Example 23 Let1198671= ⟨[03 04] [01 02]⟩ ⟨[03 05] [02
04]⟩ and1198672= ⟨[03 04] [02 03]⟩ be two HIVIFNs and
then
119878 (1198671) =
1
2times ⟨[03 + 03 04 + 05] [01 + 02 02 + 04]⟩
= ⟨[030 045] [015 030]⟩
119878 (1198672) = ⟨[03 04] [02 03]⟩
(12)According to Definitions 5 and 6
119871 (119878 (1198671))
=030 + 045 minus 030 times (1 minus 045) minus 015 times (1 minus 030)
2
= 024
119871 (119878 (1198672))
=03 + 04 minus 03 times (1 minus 04) minus 02 times (1 minus 03)
2
= 019
(13)
Hence 119878(1198671) gt 119878(119867
2) which indicates that 119867
1is preferred
to1198672
4 HIVIFN Aggregation Operators and TheirApplications in MCDM Problems
In this section HIVIFN aggregation operators are proposedand some properties of these operators are discussed Inparticular some hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators are proposed based on alge-braic 119905-conorms and 119905-norms Then how to utilize theseoperators to MCDM problems is discussed as well
41 HIVIFN Aggregation Operators
Definition 24 Let 119867119895(119895 = 1 2 119899) be a collection of
HIVIFNs and HIVIFNWA HIVIFNS119899 rarr HIVIFNS andthen
HIVIFNWA119908(1198671 1198672 119867
119899) =
119899
⨁119895=1
119908119895119867119895 (14)
The HIVIFNWA operator is called the HIVIFN weightedaveraging operator of dimension 119899 where 119908 = (119908
1 1199082
119908119899) is the weight vector of 119867
119895(119895 = 1 2 119899) with 119908
119895ge
0 (119895 = 1 2 119899) and sum119899119895=1
119908119895= 1
Theorem 25 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs and let119908 = (1199081 1199082 119908
119899)
be the weight vector of 119867119895(119895 = 1 2 119899) with 119908
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
23 HFSs
Definition 11 (see [44]) Let119883 be a universal set and aHFS on119883 is in terms of a function that when applied to119883will returna subset of [0 1] which can be represented as follows
where ℎ119864(119909) is a set of values in [0 1] denoting the possible
membership degrees of the element 119909 isin 119883 to the set 119864 ℎ119864(119909)
is called a hesitant fuzzy element (HFE) [23] and 119867 is theset of all HFEs It is noteworthy that if 119883 contains only oneelement then 119864 is called a hesitant fuzzy number (HFN)briefly denoted by 119864 = ℎ
119864(119909) The set of all hesitant fuzzy
numbers is represented as HFNSTorra [44] defined some operations on HFNs and Xia
and Xu [19 22] defined some new operations on HFNs andthe score function
Definition 12 (see [43]) Let ℎ ℎ1 and ℎ
2be three HFNs 120582 ge
0 and then four operations are defined as follows
(1) ℎ120582 = ⋃120574isinℎ
119896minus1(120582119896(120574))
(2) 120582ℎ = ⋃120574isinℎ
119897minus1(120582119897(120574))
(3) ℎ1oplus ℎ2= ⋃1205741isinℎ11205742isinℎ2
119897minus1(119897(1205741) + 119897(120574
2))
(4) ℎ1otimes ℎ2= ⋃1205741isinℎ11205742isinℎ2
119896minus1(119896(1205741) + 119896(120574
2))
Here 119897(119905) = 119896(1 minus 119905) and 119896 [0 1] rarr [0infin) is a strictlydecreasing function
Definition 13 (see [19]) Let ℎ isin HFNs and 119904(ℎ) =
(1ℎ)sum120574isinℎ
120574 is called the score function of ℎ where ℎ isthe number of elements in ℎ For two HFNs ℎ
1and ℎ
2 if
119904(ℎ1) gt 119904(ℎ
2) then ℎ
1gt ℎ2 if 119904(ℎ
1) = 119904(ℎ
2) then ℎ
1= ℎ2
Example 14 Let ℎ1= 03 05 06 ℎ
2= 04 07 be two
HFNs According to Definition 13 119904(ℎ1) = (13)times (03+05+
06) = 04667 119904(ℎ2) = 055 119904(ℎ
2) gt 119904(ℎ
1) so ℎ
2gt ℎ1
Furthermore Torra and Narukawa [18 44] proposed anaggregation principle for HFEs
Definition 15 (see [18 44]) Let 119864 = ℎ1 ℎ2 ℎ
119899 be a set of
119899 HFEs let 120599 be a function on 119864 and let 120599 [0 1]119899 rarr [0 1]and then
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 In some cases decision-makers
usually cannot estimate criteria values of alternatives with anexact numerical value when the information is not preciselyknown Therefore interval values in fuzzy sets can representit better than specific numbers such as IVFSs and IVIFSsFurthermore IVIFSs could describe the object being ldquoneitherthis nor thatrdquo and the membership degree and nonmember-ship degree of IVIFSs are interval values respectively Thusprecise numerical values in HFSs can be replaced by IVIFSswhich are more flexible in the real world and this is what thissection will solve
Definition 16 Assume that 119883 is a finite universal set AHIVIFS 119860 in119883 is an object in the following form
where 119867119864(119909) is a finite set of values in IVIFSs denoting the
possiblemembership degrees andnonmembership degrees ofthe element 119909 isin 119883 to the set 119864
Based on the definition given above
119867119864 (119909) =
119899(119867119864(119909))
⋃119894=1
⟨[120583119871
119864119894
(119909) 120583119880
119864119894
(119909)] []119871119864119894
(119909) ]119880119864119894
(119909)]⟩
(9)
where 0 le 120583119871
1198641
(119909) le 120583119880
1198641
(119909) le 120583119871
1198642
(119909) le 120583119880
1198642
(119909) le sdot sdot sdot
120583119871
119899(119867119864(119909))
(119909) le 120583119880
119899(119867119864(119909))
(119909) le 1 0 le 120583119880
119864119894
(119909) + ]119880119864119894
(119909) le 1120583119871
119864119894
(119909) ge 0 ]119871119864119894
(119909) ge 0 and 119899(119867119864(119909)) ge 1 Actually HIVIFSs
have several possible membership degrees taking the formof IVIFSs instead of FSs in HFSs If 119899(119867
119864(119909)) = 1 then
the HIVIFS is reduced to an IVIFS if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) = ]119880119864119894
(119909) = 0 (119894 = 1 2
119899(119867119864(119909))) then the HIVIFS is reduced to a HFS if 120583119871
119864119894
(119909) =
120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) or ]119871
119864119894
(119909) = ]119880119864119894
(119909) (119894 =
1 2 119899(119867119864(119909))) then the HIVIFS is reduced to a HIVFS
if 120583119871119864119894
(119909) = 120583119880
119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) and ]119871
119864119894
(119909) =
]119880119864119894
(119909) (119894 = 1 2 119899(119867119864(119909))) then the HIVIFS is reduced
to a HIFS Furthermore 119867119864(119909) is called a hesitant interval-
valued intuitionistic fuzzy element (HIVIFE) and 119864 is theset of all HIVIFEs In particular if 119883 has only one element⟨119909119867119864(119909)⟩ is called a hesitant interval-valued intuitionistic
Based on Definitions 5 6 and 13 the ranking method forHIVIFNs is defined as follows
Definition 22 Let 119867 isin HIVIFNs 119878(119867) = (1119867)sum120574isin119867
120574 iscalled the score function of 119867 where 119867 is the number ofthe interval-valued intuitionistic fuzzy values in 119867 For twoHIVIFNs 119867
1and 119867
2 if 119878(119867
1) gt 119878(119867
2) then 119867
1gt 1198672 if
119878(1198671) = 119878(119867
2) then119867
1= 1198672
Note that 119878(1198671) and 119878(119867
2) could be compared by utilizing
Definitions 5 and 6
Example 23 Let1198671= ⟨[03 04] [01 02]⟩ ⟨[03 05] [02
04]⟩ and1198672= ⟨[03 04] [02 03]⟩ be two HIVIFNs and
then
119878 (1198671) =
1
2times ⟨[03 + 03 04 + 05] [01 + 02 02 + 04]⟩
= ⟨[030 045] [015 030]⟩
119878 (1198672) = ⟨[03 04] [02 03]⟩
(12)According to Definitions 5 and 6
119871 (119878 (1198671))
=030 + 045 minus 030 times (1 minus 045) minus 015 times (1 minus 030)
2
= 024
119871 (119878 (1198672))
=03 + 04 minus 03 times (1 minus 04) minus 02 times (1 minus 03)
2
= 019
(13)
Hence 119878(1198671) gt 119878(119867
2) which indicates that 119867
1is preferred
to1198672
4 HIVIFN Aggregation Operators and TheirApplications in MCDM Problems
In this section HIVIFN aggregation operators are proposedand some properties of these operators are discussed Inparticular some hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators are proposed based on alge-braic 119905-conorms and 119905-norms Then how to utilize theseoperators to MCDM problems is discussed as well
41 HIVIFN Aggregation Operators
Definition 24 Let 119867119895(119895 = 1 2 119899) be a collection of
HIVIFNs and HIVIFNWA HIVIFNS119899 rarr HIVIFNS andthen
HIVIFNWA119908(1198671 1198672 119867
119899) =
119899
⨁119895=1
119908119895119867119895 (14)
The HIVIFNWA operator is called the HIVIFN weightedaveraging operator of dimension 119899 where 119908 = (119908
1 1199082
119908119899) is the weight vector of 119867
119895(119895 = 1 2 119899) with 119908
119895ge
0 (119895 = 1 2 119899) and sum119899119895=1
119908119895= 1
Theorem 25 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs and let119908 = (1199081 1199082 119908
119899)
be the weight vector of 119867119895(119895 = 1 2 119899) with 119908
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
Based on Definitions 5 6 and 13 the ranking method forHIVIFNs is defined as follows
Definition 22 Let 119867 isin HIVIFNs 119878(119867) = (1119867)sum120574isin119867
120574 iscalled the score function of 119867 where 119867 is the number ofthe interval-valued intuitionistic fuzzy values in 119867 For twoHIVIFNs 119867
1and 119867
2 if 119878(119867
1) gt 119878(119867
2) then 119867
1gt 1198672 if
119878(1198671) = 119878(119867
2) then119867
1= 1198672
Note that 119878(1198671) and 119878(119867
2) could be compared by utilizing
Definitions 5 and 6
Example 23 Let1198671= ⟨[03 04] [01 02]⟩ ⟨[03 05] [02
04]⟩ and1198672= ⟨[03 04] [02 03]⟩ be two HIVIFNs and
then
119878 (1198671) =
1
2times ⟨[03 + 03 04 + 05] [01 + 02 02 + 04]⟩
= ⟨[030 045] [015 030]⟩
119878 (1198672) = ⟨[03 04] [02 03]⟩
(12)According to Definitions 5 and 6
119871 (119878 (1198671))
=030 + 045 minus 030 times (1 minus 045) minus 015 times (1 minus 030)
2
= 024
119871 (119878 (1198672))
=03 + 04 minus 03 times (1 minus 04) minus 02 times (1 minus 03)
2
= 019
(13)
Hence 119878(1198671) gt 119878(119867
2) which indicates that 119867
1is preferred
to1198672
4 HIVIFN Aggregation Operators and TheirApplications in MCDM Problems
In this section HIVIFN aggregation operators are proposedand some properties of these operators are discussed Inparticular some hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators are proposed based on alge-braic 119905-conorms and 119905-norms Then how to utilize theseoperators to MCDM problems is discussed as well
41 HIVIFN Aggregation Operators
Definition 24 Let 119867119895(119895 = 1 2 119899) be a collection of
HIVIFNs and HIVIFNWA HIVIFNS119899 rarr HIVIFNS andthen
HIVIFNWA119908(1198671 1198672 119867
119899) =
119899
⨁119895=1
119908119895119867119895 (14)
The HIVIFNWA operator is called the HIVIFN weightedaveraging operator of dimension 119899 where 119908 = (119908
1 1199082
119908119899) is the weight vector of 119867
119895(119895 = 1 2 119899) with 119908
119895ge
0 (119895 = 1 2 119899) and sum119899119895=1
119908119895= 1
Theorem 25 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs and let119908 = (1199081 1199082 119908
119899)
be the weight vector of 119867119895(119895 = 1 2 119899) with 119908
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
Based on Definitions 5 6 and 13 the ranking method forHIVIFNs is defined as follows
Definition 22 Let 119867 isin HIVIFNs 119878(119867) = (1119867)sum120574isin119867
120574 iscalled the score function of 119867 where 119867 is the number ofthe interval-valued intuitionistic fuzzy values in 119867 For twoHIVIFNs 119867
1and 119867
2 if 119878(119867
1) gt 119878(119867
2) then 119867
1gt 1198672 if
119878(1198671) = 119878(119867
2) then119867
1= 1198672
Note that 119878(1198671) and 119878(119867
2) could be compared by utilizing
Definitions 5 and 6
Example 23 Let1198671= ⟨[03 04] [01 02]⟩ ⟨[03 05] [02
04]⟩ and1198672= ⟨[03 04] [02 03]⟩ be two HIVIFNs and
then
119878 (1198671) =
1
2times ⟨[03 + 03 04 + 05] [01 + 02 02 + 04]⟩
= ⟨[030 045] [015 030]⟩
119878 (1198672) = ⟨[03 04] [02 03]⟩
(12)According to Definitions 5 and 6
119871 (119878 (1198671))
=030 + 045 minus 030 times (1 minus 045) minus 015 times (1 minus 030)
2
= 024
119871 (119878 (1198672))
=03 + 04 minus 03 times (1 minus 04) minus 02 times (1 minus 03)
2
= 019
(13)
Hence 119878(1198671) gt 119878(119867
2) which indicates that 119867
1is preferred
to1198672
4 HIVIFN Aggregation Operators and TheirApplications in MCDM Problems
In this section HIVIFN aggregation operators are proposedand some properties of these operators are discussed Inparticular some hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators are proposed based on alge-braic 119905-conorms and 119905-norms Then how to utilize theseoperators to MCDM problems is discussed as well
41 HIVIFN Aggregation Operators
Definition 24 Let 119867119895(119895 = 1 2 119899) be a collection of
HIVIFNs and HIVIFNWA HIVIFNS119899 rarr HIVIFNS andthen
HIVIFNWA119908(1198671 1198672 119867
119899) =
119899
⨁119895=1
119908119895119867119895 (14)
The HIVIFNWA operator is called the HIVIFN weightedaveraging operator of dimension 119899 where 119908 = (119908
1 1199082
119908119899) is the weight vector of 119867
119895(119895 = 1 2 119899) with 119908
119895ge
0 (119895 = 1 2 119899) and sum119899119895=1
119908119895= 1
Theorem 25 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs and let119908 = (1199081 1199082 119908
119899)
be the weight vector of 119867119895(119895 = 1 2 119899) with 119908
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
42 HIVIFN Algebraic Aggregation Operators and HIV-IFN Einstein Aggregation Operators Obviously different 119905-conorms and 119905-norms may lead to different aggregationoperators In the following HIVIFN algebraic aggregationoperators and Einstein aggregation operators are presentedbased on algebraic norms and Einstein norms
Theorem 36 Let 119867119895= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 = 1 2
119899) be a collection of HIVIFNs let 119908 = (1199081 1199082 119908
119899)119879
be the weight vector of 119860119895(119895 = 1 2 119899) with 120582 gt 0
119895=1119908119895= 1 119896(119909) = minus ln(119909) and
119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1minus119909) 119897minus1(119909) = 1minus119890
minus119909 119879(119909 119910) = 119909119910
and 119878(119909 119910) = 1 minus ((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and119905-norm Then some HIVIFN algebraic aggregation operatorscould be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted averaging operator is as follows
HIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus
119899
prod119895=1
(1 minus 119886119894119895
)119908119895
1 minus
119899
prod119895=1
(1 minus 119887119894119895
)119908119895
]
]
[
[
119899
prod119895=1
(119888119894119895
)119908119895
119899
prod119895=1
(119889119894119895
)119908119895
]
]
⟩
(37)
(2) Hesitant interval-valued intuitionistic fuzzy numberalgebraic weighted geometric operator is as follows
(4) Hesitant interval-valued intuitionistic fuzzy number alge-braic weighted arithmetic geometric operator is as follows
HIVIFNAWAG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)2
)119908119895
)
12
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)2
)119908119895
)
12
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)2
)119908119895
)
12
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)2
)119908119895
)
12
]
]
⟩
(40)
(5) Generalized hesitant interval-valued intuitionisticfuzzy number algebraic weighted averaging operator is asfollows
The Scientific World Journal 13
GHIVIFNAWA119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
(1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119888119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(41)
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
(6) Generalized hesitant interval-valued intuitionistic fuzzynumber algebraic weighted geometric operator is as follows
GHIVIFNAWG119908(1198671 1198672 119867
119899)
=
119899(1198671)
⋃1198941=1
sdot sdot sdot
119899(119867119899)
⋃119894119899=1
⟨[
[
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119886119894119895
)120582
)
119908119895
)
1120582
1 minus (1 minus
119899
prod119895=1
(1 minus (1 minus 119887119894119895
)120582
)
119908119895
)
1120582
]
]
[
[
(1 minus
119899
prod119895=1
(1 minus (119888119894119895
)120582
)
119908119895
)
1120582
(1 minus
119899
prod119895=1
(1 minus (119889119894119895
)120582
)
119908119895
)
1120582
]
]
⟩
(42)
In particular if 120582 = 1 then (41) is reduced to (37) and (42) isreduced to (38) if 120582 = 2 then (41) is reduced to (39) and (42)is reduced to (40)
Example 37 Let1198671= ⟨[02 03] [01 02]⟩ ⟨[04 05] [02
03]⟩ and1198672= ⟨[01 03] [02 04]⟩ be two HIVIFNs and
let 119908 = (04 06) be the weight of them and 120582 = 1 2 5According toTheorem 36 the following can be calculated
(1) HIVIFNAWA119908(1198671 1198672) = ⟨[1 minus (1 minus 02)
04times
(1 minus 01)06 1minus(1 minus 03)
04times(1 minus 03)
06] [01
04times0206 0204times
0406] [1 minus (1 minus 04)
04times (1 minus 01)
06 1 minus (1 minus 05)
04times
(1 minus 03)06] [02
04times 0206 0304
times 0406]⟩ = ⟨[01414
03000] [01516 03031]⟩ ⟨[02347 03881] [02000
03565]⟩ Consider
HIVIFNAWG119908(1198671 1198672)
= ⟨[01320 03000] [01614 03268]⟩
⟨[01741 03680] [02000 03618]⟩
(43)
According to Definitions 24 6 and 8 Consider the following
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
In the three cases listed above the aggregation resultsby using the GHIVIFNAWA operator are greater than theaggregation results by utilizing the GHIVIFNAWG operator
Theorem 38 Let 119867119895
= ⋃119899(119867119895)
119894119895=1
⟨[119886119894119895
119887119894119895
] [119888119894119895
119889119894119895
]⟩ (119895 =
1 2 119899) be a collection of HIVIFNs and let 119908 = (1199081 1199082
119908119899)119879 be the weight vector of 119860
119895(119895 = 1 2 119899) with 120582 gt
0 119908119895ge 0 (119895 = 1 2 119899) and sum119899
119895=1119908119895= 1 119896(119909) = ln((2 minus
119909)119909) and 119896minus1(119909) = 2(119890119909+1) 119897(119909) = ln((2minus(1minus119909))(1minus119909))
119897minus1(119909) = 1 minus (2(119890
119909+ 1))119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910))
and 119878(119909 119910) = (119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and119905-norm respectively Then some HIVIFN Einstein aggregationoperators could be obtained as follows
(1) Hesitant interval-valued intuitionistic fuzzy numberEinstein weighted averaging operator is as follows
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
In particular if 120582 = 1 then (58) is reduced to (52) and (60) isreduced to (53) if 120582 = 2 then (58) is reduced to (54) and (60)is reduced to (56)
43 The MCDM Approach Based on the GHIVIFNWA andGHIVIFNWG Operators Let 119860 = 119886
1 1198862 119886119898 be a finite
set of alternatives and let 119862 = 1198881 1198882 119888119899 be a finite set of
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
Step 2 Calculate the score values According toDefinition 22 the score of overall values 119878(119910
119894) (119894 = 1 2 119898)
could be calculated
Step 3 Rank the preference order of all alternatives 119886119894(119894 =
1 2 119898) 119871(119878(119910119894)) (119894 = 1 2 119898) could be obtained
according to Definition 5 The greater the value of 119871(119878(119910119894))
is the better the alternative 119886119894(119894 = 1 2 119898) will be
Step 4 Select the optimal one(s)
5 Illustrative Example
In this section the proposed approach and one existingmethod are utilized to evaluate four companies with hesitantinterval-valued intuitionistic fuzzy information
The enterprisersquos board of directors intends to find anautomobile company and establish a foundation for deeperand more extensive cooperation with it in the following fiveyears Suppose there are four possible projects 119886
119894(119894 = 1 2 3 4)
to be evaluated It is necessary to compare these companiesand rank them in terms of their importance Four criteriasuggested by the Balanced Scorecard methodology couldbe taken into account (it should be noted that all of them
are of the maximization type) 1198881 economy 119888
2 comfort 119888
3
design and 1198884 safety And suppose that the weight vector of
the criteria is 119908 = (02 03 015 035) The decision-makersare required to provide their evaluation of the company 119886
119894
under the criterion 119888119895(119894 = 1 2 3 4 119895 = 1 2 3 4) The
1 2 119899) of the alternative 119886119894(119894 = 1 2 119898)
For the convenience of analysis and computation weuse hesitant interval-valued intuitionistic fuzzy algebraicaggregation operators to fuse the attribute values which arerepresented in the form of HIVIFNs in MCDM problemsLet 119896(119909) = minus ln(119909) and let 119896minus1(119909) = 119890
minus119909 119897(119909) = minus ln(1 minus
119909) 119897minus1(119909) = 1 minus 119890
minus119909 and 119879(119909 119910) = 119909119910 and 119878(119909 119910) = 1 minus
((1 minus 119909)(1 minus 119910)) be algebraic 119905-conorm and 119905-normThen theGHIVIFNWA or GHIVIFNWG operators are respectivelyreduced to the GHIVIFNAWA or GHIVIFNAWG operatorsand (62) and (63) are reduced to the following expression thatis
119910119894= GHIVIFNAWA (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
(1 minus
4
prod119895=1
(1 minus (119886119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119888119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(64)
or
119910119894= GHIVIFAWG (
1198941 1198942 1198943 1198944)
=
119899(1198941)
⋃1198941=1
sdot sdot sdot
119899(1198944)
⋃1198944=1
⟨[
[
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119886119896
119894119895)120582
)119908119895
)
1120582
1 minus (1 minus
4
prod119895=1
(1 minus (1 minus 119887119896
119894119895)120582
)119908119895
)
1120582
]
]
[
[
(1 minus
4
prod119895=1
(1 minus (119888119896
119894119895)120582
)119908119895
)
1120582
(1 minus
4
prod119895=1
(1 minus (119889119896
119894119895)120582
)119908119895
)
1120582
]
]
⟩
(119894 = 1 2 3 4)
(65)
Let 120582 = 2 and according to the formula listed above theoverall HIVIFNs 119910
119894of the alternatives 119886
119894(119894 = 1 2 3 4) could
be obtained and shown in Table 2
Step 2 Based on Definition 22 and Table 2 the score valuesof overall HIVIFNs 119878(119910
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
So the final ranking of alternatives is 1198862≻ 1198864≻ 1198863≻ 1198861
Step 4 Select the best one(s) In Step 3 if the GHIVIFNAWAoperator is utilized then the optimal alternative is 119886
2while the
worst alternative is 1198861 if the GHIVIFNAWGoperator is used
then the optimal alternative is 1198864while the worst alternative
is 1198861
52 Sensitivity Analysis In Step 1 two aggregation operatorscan be used and the sensitivity analysis will be conducted inthese following cases
(1)The hesitant interval-valued intuitionistic fuzzy alge-braic aggregation operators in Step 1 are illustrated as follows
In order to investigate the influence of 120582 on the rankingof alternatives different 120582 are utilizedThe ranking results areshown in Tables 4 and 5
From Tables 4 and 5 the GHIVIFNAWA and GHIV-IFNAWG operators have produced different rankings ofthe alternatives However for each operator the rankingsobtained are consistent as 120582 changes Moreover 119886
4or 1198862is
always the optimal one while the worst one is always 1198861
(2) The hesitant interval-valued intuitionistic fuzzy Ein-stein aggregation operators in Step 1 are illustrated as follows
Let 119896(119909) = ln((2 minus 119909)119909) and let 119896minus1(119909) = 2(119890119909+
1) 119897(119909) = ln((2 minus (1 minus 119909))(1 minus 119909)) 119897minus1(119909) = 1 minus (2(119890119909+
1)) 119879(119909 119910) = 119909119910(1 + (1 minus 119909)(1 minus 119910)) and 119878(119909 119910) =
(119909 + 119910)(1 + 119909119910) be the Einstein 119905-conorm and 119905-normrespectively Then the GHIVIFNWA and GHIVIFNWGoperators are respectively reduced to the GHIVIFNEWAand GHIVIFNEWG operators According to (58) and (60)the following results could be obtained and shown in Tables6 and 7
From Tables 6 and 7 the GHIVIFNEWA and GHIV-IFNEWG operators have produced different rankings of thealternatives Furthermore for each operator the aggrega-tion parameter 120582 also leads to different aggregation resultsbut the final rankings of alternatives are the same as theparameter changes What is more regardless of using theGHIVIFNEWA and GHIVIFNEWG operators is that 119886
4or
1198862is always the optimal one while the worst one is always 119886
1
It can be concluded from the sensitivity analysis thatdifferent 119905-conorms and 119905-norms could lead to differentaggregation results However the rankings using each opera-tor are consistent
53 Comparison Analysis Based on the same decision-making problem if the method of Chen et al [16] isemployed HIVIFNs are transformed to IVIFNs by using thescore function firstly and then IVIFNs could be aggregated bythe interval-valued intuitionistic fuzzy weighted aggregationoperators proposed by Chen et al [16]
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
1198864 The ranking here is the same as the result using the
GHIVIFNAWA and GHIVIFNAWA operatorsAccording to the calculation results although the existing
method can produce the same result as the proposedmethodthe method being compared has a problem that how to trans-form HIVIFNs to IVIFNs in the first step could avoid infor-mation loss in the process of transformation By contrastthe proposed approach based on different 119905-conorms and 119905-norms can be used to deal with different relationships amongthe aggregated arguments could handle MCDM problemsin a flexible and objective manner under hesitant interval-valued intuitionistic fuzzy environment and can providemore choices for decision-makers Additionally different 119905-conorms and 119905-norms and aggregation operators could bechosen in the practical decision-making process At the sametime different results may be produced which reflectedthe preferences of decision-makers Therefore the developedapproach can produce better results than the existingmethod
20 The Scientific World Journal
Table 5 Rankings obtained using the GHIVIFNAWG operator
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
HFSs are the extension of traditional fuzzy sets and theirmembership degree of an element is a set of several possiblevalues between 0 and 1 IVIFSs can describe the fuzzyconcept ldquoneither this nor thatrdquo and the membership degreesand nonmembership degrees of IVIFSs are not only realnumbers but interval values respectively Precise numericalvalues in HFSs can be replaced by IVIFSs which providemore preference information for decision-makers In thispaper the definition of HIVIFSs was developed and appliedto the MCDM problems in which the evaluation valuesof alternatives on criteria were expressed with HIVIFNsFurthermore based on 119905-conorms and 119905-norms some aggre-gation operators namely the HIVIFNWA and HIVIFNWGHIVIFNWAA and HIVIFNWGA and GHIVIFNWA andGHIVIFNWG operators were proposed respectively Theirproperties were discussed in detail as well In particular thecorresponding hesitant interval-valued intuitionistic fuzzyalgebraic aggregation operators based on algebraic 119905-conormand 119905-norm and hesitant interval-valued intuitionistic fuzzyEinstein aggregation operators based on Einstein 119905-conormand 119905-normwere presented In addition different aggregationoperators were utilized to fuse the hesitant interval-valuedintuitionistic fuzzy information to get the overall HIVIFNs of
alternatives and the ranking of all given alternatives At lastthe example was presented to illustrate the fuzzy decision-making process and the sensitivity analysis and comparisonanalysis were conducted to enrich the paper The prominentfeature of the proposed method is that it could providea useful and flexible way to efficiently facilitate decision-makers under a hesitant interval-valued intuitionistic fuzzyenvironment and the related calculations are simple Henceit has enriched and developed the theories and methodsof MCDM problems and also has provided a new idea forsolvingMCDMproblems In the future research the distanceand similarity measure of HIVIFSs will be studied to solveMCDM problems
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors thank the editors and anonymous reviewersfor their helpful comments and suggestions This work issupported by the National Natural Science Foundation of
The Scientific World Journal 21
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
China (nos 71271218 and 71221061) the Research Projectof Education of Hubei (no Q20122302) and the ScienceFoundation for Doctors of Hubei University of AutomotiveTechnology (no BK201405)
References
[1] L A Zadeh ldquoFuzzy setsrdquo Information and Control vol 8 no 3pp 338ndash356 1965
[2] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo VII ITKRrsquos SessionSofia 1983
[3] K T Atanassov ldquoIntuitionistic fuzzy setsrdquo Fuzzy Sets andSystems vol 20 no 1 pp 87ndash96 1986
[4] K Atanassov and G Gargov ldquoInterval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 31 no 3 pp 343ndash3491989
[5] Z S Xu ldquoIntuitionistic fuzzy aggregation operatorsrdquo IEEETransactions on Fuzzy Systems vol 15 no 6 pp 1179ndash1187 2007
[6] Z S Xu and J Chen ldquoAn approach to group decision makingbased on interval-valued intuitionistic judgment matricesrdquoSystem Engineering Theory and Practice vol 27 no 4 pp 126ndash133 2007
[7] J Ye ldquoMulticriteria fuzzy decision-making method based ona novel accuracy function under interval-valued intuitionisticfuzzy environmentrdquo Expert Systems with Applications vol 36no 3 pp 6899ndash6902 2009
[8] J Wu H B Huang and Q W Cao ldquoResearch on AHP withinterval-valued intuitionistic fuzzy sets and its application inmulti-criteria decision making problemsrdquo Applied Mathemat-ical Modelling vol 37 no 24 pp 9898ndash9906 2013
[9] K T Atanassov ldquoOperators over interval valued intuitionisticfuzzy setsrdquo Fuzzy Sets and Systems vol 64 no 2 pp 159ndash1741994
[10] W Lee ldquoA novel method for ranking interval-valued intuition-istic fuzzy numbers and its application to decision makingrdquoin Proceedings of the International Conference on IntelligentHuman-Machine Systems and Cybernetics (IHMSC rsquo09) vol 2pp 282ndash285 Zhejiang China August 2009
[11] W Lee ldquoAn enhanced multicriteria decision-making methodof machine design schemes under interval-valued intuitionisticfuzzy environmentrdquo in Proceedings of the 10th IEEE Interna-tional Conference on Computer-Aided Industrial Design andConceptual Design (CD rsquo2009) pp 721ndash725 Wenzhou ChinaNovember 2009
[12] D F Li ldquoTOPSIS-based nonlinear-programming methodologyfor multiattribute decision making with interval-valued intu-itionistic fuzzy setsrdquo IEEE Transactions on Fuzzy Systems vol18 no 2 pp 299ndash311 2010
[13] JH Park I Y Park Y CKwun andXG Tan ldquoExtension of theTOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environmentrdquo Applied MathematicalModelling vol 35 no 5 pp 2544ndash2556 2011
[14] T Y Chen H P Wang and Y Y Lu ldquoA multicriteria groupdecision-making approach based on interval-valued intuition-istic fuzzy sets a comparative perspectiverdquo Expert Systems withApplications vol 38 no 6 pp 7647ndash7658 2011
[15] V L G Nayagam and G Sivaraman ldquoRanking of interval-valued intuitionistic fuzzy setsrdquoApplied SoftComputing Journalvol 11 no 4 pp 3368ndash3372 2011
[16] S M Chen LW Lee H C Liu and SW Yang ldquoMultiattributedecision making based on interval-valued intuitionistic fuzzy
valuesrdquo Expert Systems with Applications vol 39 no 12 pp10343ndash10351 2012
[17] F Y Meng C Q Tan and Q Zhang ldquoThe induced generalizedinterval-valued intuitionistic fuzzy hybrid Shapley averagingoperator and its application in decision makingrdquo Knowledge-Based Systems vol 42 pp 9ndash19 2013
[18] V Torra andYNarukawa ldquoOn hesitant fuzzy sets and decisionrdquoin Proceedings of the IEEE International Conference on FuzzySystems pp 1378ndash1382 Jeju Island Republic of Korea August2009
[19] MMXia andZ S Xu ldquoHesitant fuzzy information aggregationin decision makingrdquo International Journal of ApproximateReasoning vol 52 no 3 pp 395ndash407 2011
[20] B Zhu Z S Xu and M M Xia ldquoHesitant fuzzy geometricBonferroni meansrdquo Information Sciences vol 205 pp 72ndash852012
[21] G W Wei ldquoHesitant fuzzy prioritized operators and theirapplication to multiple attribute decision makingrdquo Knowledge-Based Systems vol 31 pp 176ndash182 2012
[22] Z S Xu and M M Xia ldquoDistance and similarity measures forhesitant fuzzy setsrdquo Information Sciences vol 181 no 11 pp2128ndash2138 2011
[23] Z S Xu and M M Xia ldquoOn distance and correlation measuresof hesitant fuzzy informationrdquo International Journal of Intelli-gent Systems vol 26 no 5 pp 410ndash425 2011
[24] N Chen Z S Xu and M M Xia ldquoCorrelation coefficients ofhesitant fuzzy sets and their applications to clustering analysisrdquoApplied Mathematical Modeling vol 37 no 4 pp 2197ndash22112013
[25] DH Peng C Y Gao and Z F Gao ldquoGeneralized hesitant fuzzysynergetic weighted distance measures and their applicationto multiple criteria decision-makingrdquo Applied MathematicalModeling vol 37 no 8 pp 5837ndash5850 2013
[26] N Zhang and G W Wei ldquoExtension of VIKOR method fordecision making problem based on hesitant fuzzy setrdquo AppliedMathematical Modeling vol 37 no 7 pp 4938ndash4947 2013
[27] Z M Zhang ldquoHesitant fuzzy power aggregation operators andtheir application to multiple attribute group decision makingrdquoInformation Sciences vol 234 pp 150ndash181 2013
[28] N Chen Z S Xu and M M Xia ldquoInterval-valued hesitantpreference relation relations and their applications to groupdecision makingrdquo Knowledge-Based Systems vol 37 pp 528ndash540 2013
[29] G W Wei ldquoSome hesitant interval-valued fuzzy aggregationoperators and their applications to multiple attribute decisionmakingrdquo Knowledge-Based Systems vol 46 pp 43ndash53 2013
[30] G W Wei and X F Zhao ldquoInduced hesitant interval-valuedfuzzy Einstein aggregation operators and their application tomultiple attribute decision makingrdquo Journal of Intelligent andFuzzy Systems vol 24 no 4 pp 789ndash803 2013
[31] B Zhu Z S Xu and M M Xia ldquoDual hesitant fuzzy setsrdquoJournal of Applied Mathematics vol 2012 Article ID 879629 13pages 2012
[32] A Sengupta and T K Pal ldquoOn comparing interval numbersrdquoEuropean Journal of Operational Research vol 127 no 1 pp 28ndash43 2000
[33] P Y Chen ldquoAn interval estimation for the number of signalsrdquoSignal Processing vol 85 no 8 pp 1623ndash1633 2005
[34] Z S Xu ldquoDependent uncertain ordered weighted aggregationoperatorsrdquo Information Fusion vol 9 no 2 pp 310ndash316 2008
22 The Scientific World Journal
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010
[35] N N Karnik and J M Mendel ldquoCentroid of a type-2 fuzzy setrdquoInformation Sciences vol 132 no 1ndash4 pp 195ndash220 2001
[36] V L GNayagam SMuralikrishnan andG Sivaraman ldquoMulti-criteria decision-making method based on interval-valuedintuitionistic fuzzy setsrdquo Expert Systems with Applications vol38 no 3 pp 1464ndash1467 2011
[37] B Schweizer and A Sklar Probabilistic Metric Spaces ElsevierNew York NY USA 1983
[38] G Klir and B Yuan Fuzzy Sets and Fuzzy Logic Theory andApplications Prentice Hall Upper Saddle River NJ USA 1995
[39] H T Nguyen and R A Walker A First Course in Fuzzy LogicCRC Press Boca Raton Fla USA 1997
[40] E P Klement and R Mesiar Logical Algebraic Analytic andProbabilistic Aspects of Triangular Norms Elsevier AmsterdamThe Netherlands 2005
[41] Z S Xu and Z D Sun ldquoPriority method for a kind of multi-attribute decision-making problemsrdquo Journal of ManagementSciences in China vol 3 no 5 pp 35ndash39 2002
[42] Z Yue ldquoDeriving decision makerrsquos weights based on distancemeasure for interval-valued intuitionistic fuzzy group decisionmakingrdquo Expert Systems with Applications vol 38 no 9 pp11665ndash11670 2011
[43] M M Xia Research on fuzzy decision information aggregationtechniques and measures [Dissertation] Southeast UniversityNanjing China 2012
[44] V Torra ldquoHesitant fuzzy setsrdquo International Journal of IntelligentSystems vol 25 no 6 pp 529ndash539 2010