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Research ArticleA Method for Selecting Enterprise’s
LogisticsOperation Mode Based on Ballou Model
Feng Li,1 Zhi-Ping Fan ,1,2 Bing-Bing Cao ,3 and ZeWang4
1School of Business Administration, Northeastern University,
Shenyang 110169, China2State Key Laboratory of Synthetical
Automation for Process Industries, Northeastern University,
Shenyang 110819, China3School of Management, Guangzhou University,
Guangzhou 510006, China4Neuso� Group Co., Ltd., Shenyang 110179,
China
Correspondence should be addressed to Bing-Bing Cao; bbcao
[email protected]
Received 7 October 2018; Accepted 19 June 2019; Published 2 July
2019
Academic Editor: Sitek Paweł
Copyright © 2019 Feng Li et al. This is an open access article
distributed under the Creative Commons Attribution License,
whichpermits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
The right choice of logistics operation mode is not only the
foundation for improving the comprehensive operation level of
anenterprise, but also an important way to improve the management
performance. Nevertheless, the study on this aspect is
stilllacking. In this paper, we develop a novel method for
selecting enterprise’s logistics operation mode. First, we give the
analysisof main types and characteristics of the logistics
operation modes. According to the two dimensions of the Ballou
model, i.e.,the importance of logistics to enterprise success and
the enterprise operation logistics capacity, we set up an
evaluation indexsystem for selecting enterprise’s logistics
operation mode through literature analysis. Then, the each index is
evaluated using thefuzzy language assessment method, and evaluation
value of each index in the two dimensions is calculated using the
2-tuple fuzzylinguistic representation model. Furthermore, a
two-dimensional matrix model for selecting enterprise’s logistics
operation modeselection is built. According to the model, the right
logistics operationmode can be selected. Finally, an example is
used to illustratethe practicality and validity of proposed
method.
1. Introduction
The management performance is an important index forthe
operation level of the enterprise [1, 2]. In many relatedinfluence
factors of the enterprise management performance,the logistics
operation mode is an important factor whichcannot be ignored [3].
It plays an important role in improvingthe enterprise management
performance [4–6]. Logisticsoperation mode refers to a way, a
policy, or an operationstandard adopted by the enterprise during
the process of theproduction and the operation.The suitable
logistics operationmode can help to improve the information
processing abilityof the enterprise logistics operation. It can
also improvethe enterprise operation level in many aspects such as
thecost and the service capacity [7, 8]. Therefore, how toselect
enterprise’s logistics operation mode is a valuable andimportant
research topic.
So far, some related research results on the logisticsoperation
mode can be seen. For example, Yao gave the
classification of the logistics operation modes and analyzedthe
advantages and disadvantages of each kind of logisticsoperation
mode. He also developed a method for selectingthe logistics
operation mode based on the AHP and the Petrinet modelling method
[9]. Su et al. studied the influencefactors for selecting the
logistics operation mode for themanufacturing enterprise based on
the interpretive structuremode (ISM).They analyzed the hierarchy
and relationship ofthe influence factors and determined the
important factorswhich can influence the logistics operation mode
selection[10]. Cui and Hertz proposed the concept of the
logisticsmode selection based on the network development and
thecore competence and provided the logistics mode selectionmethod
for three different types of enterprises [11]. Gong andDa analyzed
two types of the logistics modes, i.e., the
logisticsoutsourcingmode and the logistics
self-supportingmode.Onthis basis, considering different dominant
model, they gavethe selection methods and the logistics modes for
four kindsof logistics combination modes [12]. Yu studied the
problem
HindawiMathematical Problems in EngineeringVolume 2019, Article
ID 3985673, 9 pageshttps://doi.org/10.1155/2019/3985673
https://orcid.org/0000-0001-6778-4637https://orcid.org/0000-0002-2923-3364https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2019/3985673
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2 Mathematical Problems in Engineering
of the logistics operation mode selection of the
constructionenterprises and offered the corresponding solutions
[13].Mcfarlane et al. proposed the “customer-oriented” conceptmodel
in intelligence logistics and helped further research onthe method
for selecting the logistics operation mode [14].
It can be seen from the existing research results that thestudy
on logistics operation mode selection mainly containsthe three
aspects: the types of logistics operation modes andthe reasons for
their existence, the relationships among thelogistics operation
mode, the logistics management perfor-mance and the enterprise
competence advantage, and themethod for selecting enterprise’s
logistics operation mode.However, the research on the influence
factors or evaluationindex system of enterprise’s logistics
operation mode is stilllacking. Although the studies of [15, 16]
involve the influencefactors of logistics operation modes
selection, they did notgive the specific analysis of the index
evaluation system.Theydid not also give themethod for selecting
enterprise’s logisticsoperation mode based on the evaluation
results. Based onthis, it is necessary to further study the method
for selectingenterprise’s logistics operation mode.
To select the right logistics operation mode, this paperis to
refer to the thought of the two-dimensional matrixmodel proposed by
Ballou [17] and determines the evaluationindex sets of two
dimensions (i.e., the importance of logisticsto enterprise success
and the enterprise operation logisticscapacity) through literature
analysis. Then, the method forselecting enterprise’s logistics
operation mode is developedbased on the fuzzy linguistic assessment
method and the 2-tuple fuzzy linguistic representation model.
The remainder of this paper is organized as follows.Section 2
introduces the Ballou model. In Section 3, weset up an evaluation
index system for logistics operationmode selection. Section 4
presents the method for selectingenterprise’s logistics operation
mode. In Section 5, a numer-ical example is given to illustrate the
use of the proposedmethod. Finally, Section 6 summarizes and
highlights themain features of the proposed method.
2. Ballou Model
The logistics operationmodemainly includes three kinds:
theself-run logistics, the third party logistics, and the
logisticsalliance [9, 11, 12, 18].The self-run logistics mode
refers to theoperation mode that the enterprise meets independently
thelogistics demand of the internal and external product supplyby
independently forming a logistics center and constructingthe basic
hardware facilities such as transport equipment,warehouse,
information platform, and so on [9, 11]. The thirdparty logistics
operation mode refers to the operation modethat the enterprise
outsources their own logistics businessto the professional
logistics enterprises. In the mode, theenterprise will outsource
the logistics business which is notwithin the core business to a
more professional logisticsenterprise [9, 12]. Thus, the use of
this mode can help tonot only reduce the enterprise’s capital
investment, but alsoto let the enterprise put its energies into
professional fields.This can further improve the market
competitiveness of theenterprise. The logistics alliance operation
mode refers to
L H
L
H
IIES
IIlogistics alliance
(competitivepartner)
I self-run logistics
IVlogistics alliance(alliance leader)
IIIthe third party
logistics
EOLC
Figure 1: Ballou model.
the operation mode that two or more enterprises carry
outlong-term cooperation to meet the specific logistics
demands[18]. It is a cooperation form which is independent fromthe
transaction relationship between the enterprise and themarket. It
is also a relatively stable and long-term contractualrelationship
between the enterprises with the purpose ofrealizing win-win.
In the existing related study on logistics service,
Balloupointed out that the choice of the logistics operation
modefor enterprises to develop logistics business mainly dependson
the comprehensive evaluation results of two dimensions,i.e., the
importance of logistics to enterprise success (IIESthereafter) and
the enterprise operation logistics capacity(EOLC thereafter) [17].
IIES refers to the importance of thelogistics to the ultimate goal
in the process of the desiredgoal of an enterprise. EOLC refers to
the skill level and thehardware level of control, coordination, and
operation withrespect to all aspects of services provided by the
enterprisewhen the enterprise aims to meet various logistics
demandsof participants. Ballou proposed a two-dimensional
decisionmatrix model (Ballou model thereafter) [17], as shown
inFigure 1. This figure has four regions, i.e., I, II, III, andIV.
Region I shows that the enterprise can adopt the self-run logistics
mode, regions II and IV show the logisticsalliance mode, and region
III shows the third party logisticsmode. Obviously, the suitable
logistics operation model canbe determined according to the
enterprise’s position in thetwo-dimensional decision matrix.
It should be pointed out that the two-dimensional matrixmodel
better reflects the theoretical basis of enterprise’s logis-tics
operationmode selection. Obviously, the two dimensions(IIES and
EOLC) have practical significance; that is, the keyof logistics
operation to promoting enterprise performanceis to attach
importance to the enterprise’s own logisticsoperation ability and
the importance of logistics to theexpected enterprise strategic
objectives.
3. An Evaluation Index System for LogisticsOperation Mode
Selection
In this section, we will present an evaluation index systemfor
logistics operation mode selection. First of all, the
relatedliterature is collected, sorted, and analyzed. Then,
according
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Mathematical Problems in Engineering 3
Figure 2:The network diagram of relationships among evaluation
indices and scholars with regard to the importance of logistics to
enterprisesuccess (IIES).
to the steps of keywords determination, literature retrievaland
analysis, evaluation index summarization, sorting andmerging, name
determination and relation diagram drawing,etc., evaluation indices
of the IIES dimension and the EOLCdimension can be determined using
the literature metrology[19].
For evaluation indices of the dimension IIES, the searchkeywords
can be determined, i.e., “logistics operation modeselection” and
“importance of logistics to enterprise success”.According to the
determined keywords, 783 articles are firstsearched out in Elsevier
database, China national knowledgeinfrastructure (CNKI), and Google
Scholar. Then, 45 relatedarticles can be determined by removing the
invalid articles.Further, by refining the evaluation indices
involved in the45 articles, 124 evaluation indices are determined.
Finally,by merging same or similar items, 13 evaluation indices
areobtained. After determining the name of each evaluationindex,
according to the frequency of the evaluation indicesinvolved in
related articles, a network diagram of relation-ships among
evaluation indices and scholars can be mappedusing the UCINET 6
software, as shown in Figure 2. In thefigure, blue squares denote
the scholars who propose the
evaluation indices in the dimension IIES, and red dots denotethe
evaluation indices in the dimension IIES. It is necessaryto note
that the size of red dot represents the frequency ofthe evaluation
index in the literature. The larger the red dotis, the higher the
approval degree of the evaluation index is.According to Figure 2,
the evaluation indices with frequencyof 10 times or more in the
literature can be determined to setup the evaluation index set of
the dimension IIES, i.e., thelogistics costs ratio (𝐼𝑠1), the own
logistics advantage (𝐼𝑠2),the logistics strategic position (𝐼𝑠3),
the logistics demand level(𝐼𝑠4), the customer satisfaction (𝐼𝑠5),
and the logistics profitsratio (𝐼𝑠6).
Similarly, for evaluation indices of the dimension EOLC,the
search keywords are determined, i.e., “logistics compe-tence”,
“logistics capacity”, and “logistics capability”. Accord-ing to the
determined keywords, 935 articles are first searchedout in Elsevier
database, China national knowledge infras-tructure (CNKI), and
Google Scholar. Then, 47 relatedarticles can be determined by
removing the invalid articles.Further, by refining the evaluation
indices involved in the45 articles, 403 evaluation indices are
determined. Finally,by merging same or similar items, 32 evaluation
indexes are
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4 Mathematical Problems in Engineering
Figure 3: The network diagram of relationships among evaluation
indices and scholars with regard to the enterprise operation
logisticscapacity (EOLC).
obtained. After determining the name of each evaluationindex, a
network diagram of relationships among evaluationindices and
scholars can be mapped using the UCINET 6software, as shown in the
Figure 3. According to Figure 3,the evaluation indices with
frequency of 10 or more inthe literature can be determined to set
up the evaluationindex set of the dimension EOLC, i.e., the
logistics servicecapability (𝐼𝑐1), the service management
capability (𝐼𝑐2), theinformation processing capability (𝐼𝑐3), the
equipment andfacility capability (𝐼𝑐4), the cost management
capability (𝐼𝑐5),the logistics flexibility (𝐼𝑐6), the logistics
reliability (𝐼𝑐7), theinformation technology level (𝐼𝑐8), the quick
response ability(𝐼𝑐9), and the innovation ability (𝐼𝑐10).
It is necessary to note that there aremore literatures
aboutevaluation indices of logistics operation mode selection.
Dueto limited space, this paper focuses on citing the
literaturewhich describe the evaluation indexes comprehensively
[16,20–23]. In addition, the above evaluation index sets are
notfixed. In the process of practical application, the indices
inthe sets can be appropriately added or removed according tothe
internal and external environment of the enterprise.
To sum up, logistics operation mode selection shouldbe based on
the two dimensions and extending indices.
According to the above determined two evaluation indexsets, the
evaluation index system for logistics operationmodeselection is
shown as Table 1. The grades of importance ofthese indices in Table
1 depend on the industry to which anenterprise belongs and the
strategy that the enterprise imple-ments. To obtain weights or
importance of these indices, wecan consider inviting experts to
provide precise judgments.
Form evaluation indices of the two dimensions extracted,their
practical significance is obvious. For the IIES dimen-sion,
extracted evaluation indicators, such as logistics costsratio,
logistics strategic position, logistics demand level andlogistics
profits ratio, etc., better reflect the importance of thelogistics
to the ultimate goal in the process of the desired goalof an
enterprise. For the EOLC dimension, extracted evalu-ation
indicators, such as logistics service capability, servicemanagement
capability and information technology level,etc., better reflect
the enterprise’s own logistics operationability. In addition, these
can also be seen in practice. Forexample, in China, Jingdong has
higher performance of thetwo dimensions, so it adopts the self-run
logistics operationmode; Tmall has lower performance of the two
dimensions,so this enterprise adopts the third party logistics
operationmode; for SF Express, the difference between
performances
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Mathematical Problems in Engineering 5
Table 1: The evaluation index system for logistics operation
mode selection.
Dimensions IndicesImportance of logistics to enterprise success
(IIES) Logistics costs ratio (𝐼𝑠1)
Own logistics advantage (𝐼𝑠2)Logistics strategic position
(𝐼𝑠3)Logistics demand level (𝐼𝑠4)Customer satisfaction
(𝐼𝑠5)Logistics profits ratio (𝐼𝑠6)
Enterprise operation logistics capacity (EOLC) Logistics service
capability (𝐼𝑐1)Service management capability (𝐼𝑐2)Information
processing capability (𝐼𝑐3)Equipment and facility capability
(𝐼𝑐4)Cost management capability (𝐼𝑐5)Logistics flexibility
(𝐼𝑐6)logistics reliability (𝐼𝑐7)Information technology level
(𝐼𝑐8)Quick response ability (𝐼𝑐9)Innovation ability (𝐼𝑐10)
of the two dimensions is large, so this enterprise adopts
thelogistics alliance operation mode through cooperation
withUPS.
4. The Method of Selecting Enterprise’sLogistics Operation
Mode
Generally, the logistics operation mode selection requiresgroup
opinions from multiple experts. They are responsiblefor providing
evaluation information for the performanceand importance of each
index. From Table 1, it is obviousthat all indices for logistics
operation mode selection arequalitative, so the easiest way for
experts to express theiropinions is to use fuzzy linguistic terms
such as “VeryHigh”, “High”, or “Middle”. Therefore, the situation
thatexperts express their opinions by use of linguistic
evaluationinformation is considered in this study. Since the
preferenceinformation delivered by linguistic terms is fuzzy, we
considerusing a fuzzy linguistic assessment method to conduct
thelogistics operation mode selection.
In this study, we use the method based on the 2-tuplefuzzy
linguistic representation model [24, 25] to deal withlinguistic
information since this method can overcome theweakness that
evaluation results usually do not exactly matchany of the initial
linguistic terms and the information lossoften occurs.
In this section, we first present the approach to
determineweights of the evaluation indices and then give the
approachto comprehensive evaluation for two dimensions.
4.1. e Determination of Weights of the Evaluation Indices.With
respect to the evaluation index system of the twodimensions as
shown in Table 1, determinations of weights ofthe indices can be
obtained using the expert group evaluationmethod. In the process of
using the of the evaluation
method, the characteristics of experts from the differentareas
or departments and the function distribution of expertgroups
members should be considered. The compositionof expert group
members should cover every departmentas much as possible so as to
obtain more comprehensiveevaluation results. Usually, expert group
members can befrom the enterprise’s related department and mainly
includeenterprise senior managers and department managers who
isresponsible for the logistics, sales, etc.
According to the fuzzy language assessment theory pro-posed by
Zadeh [26], let 𝑆 = {𝑆0, 𝑆1, . . . , 𝑆𝑇} be a preestab-lished
finite and totally ordered set with odd cardinalities,where 𝑆𝑖
denotes the 𝑖th language term, 𝑆𝑖 ∈ 𝑆. In this study,we choose a
linguistic term set with seven elements (languageterm) according to
real needs, i.e., 𝑆 = {𝑆0=DL: DefinitelyLow, S1 = VL: Very Low, S2
= L: Low, S3 = M: Medium, S4 =H: High, S5 = VH: Very High, S6 = DH:
Definitely High}.
Let 𝐸 = {𝐸1, 𝐸2, . . . , 𝐸𝑚} be a finite expert set, 𝑚 ≥
2,where𝐸𝑘 denotes the 𝑘th expert who is invited by the
decisionmaker to make the evaluation. Let 𝐼𝑠 = {𝐼𝑠1, 𝐼𝑠2, . . . ,
𝐼𝑠𝑝} bethe evaluation index set in the dimension IIES, where
𝐼𝑠𝑖denotes the 𝑖th evaluation index, 𝑖 = 1, 2, . . . , 𝑝; let 𝑈𝑃
={𝑢1, 𝑢2, . . . , 𝑢𝑝} be the index weight set corresponding to
theset 𝐼𝑠 = {𝐼𝑠1, 𝐼𝑠2, . . . , 𝐼𝑠𝑝}, where 𝑢𝑖 denotes the weight
ofthe 𝑖th index, 𝑖 = 1, 2, . . . , 𝑝. Let 𝐼𝑐 = {𝐼𝑐1, 𝐼𝑐2, . . . ,
𝐼𝑐𝑞} bethe evaluation index set in the dimension EOLC, where
𝐼𝑐𝑗denotes the 𝑗th evaluation index, 𝑗 = 1, 2, . . . , 𝑝; let 𝑉𝑄
={V1, V2, . . . , V𝑞} be the index weight set corresponding to
theset 𝐼𝑐 = {𝐼𝑐1, 𝐼𝑐2, . . . , 𝐼𝑐𝑞}, where V𝑗 denotes the weight of
the𝑗th index, 𝑗 = 1, 2, . . . , 𝑝.
In this paper, we assume that the experts’ assessmentinformation
on index weights and index evaluation valuesin the two dimensions
is in the form of fuzzy linguisticterm. Suppose 𝑊𝐸 = (𝑤𝑘𝑠1, 𝑤𝑘𝑠2, .
. . , 𝑤𝑘𝑠𝑝)𝑇 and 𝑊𝑂 =(𝑤𝑘𝑐1, 𝑤𝑘𝑐2, . . . , 𝑤𝑘𝑐𝑞)𝑇 be evaluation
index weight vectors inthe IIES dimension and the EOLC dimension
provided by
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6 Mathematical Problems in Engineering
the expert 𝐸𝑘, respectively, where 𝑤𝑘𝑠𝑖 and 𝑤𝑘𝑐𝑗 are
linguisticassessments on the importance of the indices 𝐼𝑠 and 𝐼𝑐
givenby the expert 𝐸𝑘, respectively, and 𝑊𝑘𝑠𝑖,𝑊𝑘𝑐𝑗 ∈ 𝑆. Let 𝑈
=[𝑢𝑘𝑖]𝑚×𝑝 and 𝑉 = [V𝑘𝑗]𝑚×𝑞 be the evaluation matrices inthe IIES
dimension and the EOLC dimension, respectively,where 𝑢𝑘𝑖 and V𝑘𝑗
denote the fuzzy language assessments withrespect to the indexes
𝐼𝑠𝑖 and 𝐼𝑐𝑗 provided by the expert 𝐸𝑘,𝑢𝑘𝑖, V𝑘𝑗 ∈ 𝑆.
According to the related properties of the fuzzy
languageassessment term set and the calculation method based
the2-tuple fuzzy linguistic representation model [24, 25, 27],the
index weight 𝑤𝑘𝑠𝑖 and the index evaluation value 𝑢𝑘𝑖in the IIES
dimension can be converted into the 2-tuplelinguistic evaluation
information, respectively, i.e., (𝑤𝑘𝑠𝑖, 0)and (𝑢𝑘𝑖, 0). Likewise,
the evaluation matrix 𝑈 = [𝑢𝑘𝑖]𝑚×𝑝can be converted into the 2-tuple
linguistic evaluation matrix�̂� = [�̂�𝑘𝑖]𝑚×𝑝, where �̂�𝑘𝑖 = (�̂�𝑘𝑖,
0). Similarly, the indexweight 𝑤𝑘𝑐𝑗 and the index evaluation value
V𝑘𝑗 in the EOLCdimension can be also converted to the 2-tuple
linguisticevaluation information, respectively, i.e., (𝑤𝑘𝑐𝑗, 0) and
(𝑢𝑘𝑗, 0),respectively, and the evaluation matrix 𝑉 = [V𝑘𝑗]𝑚×𝑞 can
beconverted into the 2-tuple linguistic evaluation matrix �̂�
=[V̂𝑘𝑗]𝑚×𝑞, where V̂𝑘𝑗 = (V̂𝑘𝑗, 0).
By the existing calculation method [28, 29], the indexweight
vector in the IIES dimension, i.e., (𝑢𝑖, 𝛼𝑠𝑖), can beobtained by
integrating the indexweight information (𝑤𝑘𝑠𝑖, 0)and index
evaluation information (𝑢𝑘𝑖, 0). It calculationformula is as
follows:
(𝑢𝑖, 𝛼𝑠𝑖) = Δ(∑𝑝
𝑘=1Δ−1 (𝑤𝑘, 0) × Δ−1 (𝑢𝑘𝑖, 0)∑𝑝𝑘=1
Δ−1 (𝑤𝑘, 0) ) ,𝑖 = 1, 2, . . . , 𝑝.
(1)
In the same way, the index weight vector in the EOLCdimension,
i.e., (V𝑖, 𝛼𝑐𝑗), can be obtained by integrating theindex weight
information (𝑤𝑘𝑐𝑗, 0) and the index evaluationinformation (V𝑘𝑗, 0).
Its calculation formula is as follows:(V𝑖, 𝛼𝑐𝑗) = Δ(∑
𝑞
𝑘=1Δ−1 (𝑤𝑘, 0) × Δ−1 (V𝑘𝑗, 0)∑𝑞𝑘=1
Δ−1 (𝑤𝑘, 0) ) ,𝑗 = 1, 2, . . . , 𝑞.
(2)
4.2. Comprehensive Evaluation of Two Dimensions. Using thefuzzy
language term set 𝑆 = {𝑆0, 𝑆1, . . . , 𝑆𝑇}, experts 𝐸 ={𝐸1, 𝐸2, . .
. , 𝐸𝑚} can provide their evaluation informationof the performance
with respect to the each index in theevaluation index set 𝐼𝑠; thus
the evaluation matrix 𝑋 =[𝑥𝑘𝑖]𝑚×𝑝 with respect to the evaluation
index set 𝐼𝑠 can bebuilt, where 𝑥𝑘𝑖 is a fuzzy language term
selected by theexpert 𝐸𝑘, 𝑥𝑘𝑖 ∈ 𝑆, i.e., the evaluation for the
performancewith respect to the index 𝐼𝑠𝑖. In the same way, experts
𝐸 ={𝐸1, 𝐸2, . . . , 𝐸𝑚} can provide their evaluation informationof
the performance with respect to the each index in theevaluation
index set 𝐼𝑐; thus the evaluation matrix 𝑌 =[𝑦𝑘𝑗]𝑚×𝑞 with respect
to the evaluation index set 𝐼𝑐 can bebuilt, where 𝑦𝑘𝑗 is an
evaluation language term selected by the
expert 𝐸𝑘, i.e., the evaluation of the performance with
respectto the index 𝐼𝑐𝑗, 𝑦𝑘𝑗 ∈ 𝑆.
By the calculation method based on the 2-tuple fuzzylinguistic
representation model, evaluation matrices 𝑋 =[𝑥𝑘𝑖]𝑚×𝑝 and 𝑌 =
[𝑦𝑘𝑗]𝑚×𝑞 can be converted into the 2-tuplefuzzy linguistic form,
i.e., 𝑋 = [𝑥𝑘𝑖]𝑚×𝑝 and �̂� = [𝑦𝑘𝑗]𝑚×𝑞,where 𝑥𝑘𝑖 = (𝑥𝑘𝑖, 0) and 𝑦𝑘𝑗
= (𝑦𝑘𝑗, 0).
By aggregating the weight evaluation information (i.e.,(𝑤𝑘, 0))
and the performance evaluation information (i.e.,(𝑥𝑘𝑖, 0)) with
respect to the indices in the dimension IIESprovide by each expert,
the expert group’s evaluation indexvalue of the dimension IIES,
(𝑥𝑖, 𝛼𝑠𝑖), can be determined, andits calculation formula is given
by
(𝑥𝑖, 𝛼𝑠𝑖) = Δ(∑𝑚𝑘=1 Δ−1 (𝑤𝑘, 0) × Δ−1 (𝑥𝑘𝑖, 0)∑𝑚𝑘=1 Δ−1 (𝑤𝑘, 0)
) ,
𝑖 = 1, 2, . . . , 𝑝.(3)
where 𝑥𝑖 ∈ 𝑆 and 𝛼𝑠𝑡 ∈ [−0.5, 0.5). Further, by (3),the
comprehensive evaluation value of the dimension IIES,(𝑥, 𝛼𝑠), can
be determined, and its calculation formula isgiven by
(𝑥, 𝛼𝑠) = Δ(∑𝑝𝑖=1 Δ−1 (𝑢𝑖, 𝛼𝑠𝑖) × Δ−1 (𝑥𝑖, 𝛼𝑠𝑖)∑𝑝𝑖=1 Δ−1 (𝑢𝑖,
𝛼𝑠𝑖) ) , (4)
where 𝑥 ∈ 𝑆 and 𝛼𝑠 ∈ [−0.5, 0.5).In the same way, by aggregating
the weight evaluation
information (i.e., (𝑤𝑘, 0)) and the performance
evaluationinformation (i.e., (𝑦𝑘𝑗, 0)) with respect to the indices
in thedimension EOLC provided by each expert, the expert
group’sevaluation index value of the dimension EOLC, (𝑦𝑗, 𝛼𝑐𝑗),
canbe determined, and its calculation formula is given by
(𝑦𝑗, 𝛼𝑐𝑗) = Δ(∑𝑚𝑘=1 Δ−1 (𝑤𝑘, 0) × Δ−1 (𝑦𝑘𝑗, 0)∑𝑚𝑘=1 Δ−1 (𝑤𝑘, 0)
) ,
𝑗 = 1, 2, . . . , 𝑞.(5)
where 𝑦𝑗 ∈ 𝑆 and 𝛼𝑐𝑗 ∈ [−0.5, 0.5). Further, by (5),
thecomprehensive evaluation value of the dimension EOLC,(𝑦, 𝛼𝑐),
can be determined, and its calculation formula isgiven by
(𝑦, 𝛼𝑐) = Δ(∑𝑞𝑖=1 Δ−1 (V𝑗, 𝛼𝑐𝑗) × Δ−1 (𝑦𝑖, 𝛼𝑐𝑗)
∑𝑞𝑖=1 Δ−1 (V𝑗, 𝛼𝑐𝑗) ) , (6)where 𝑦 ∈ 𝑆 and 𝛼𝑐 ∈ [−0.5, 0.5).4.3.
e Selection of Logistics Operation Mode. In the Balloumodel as
shown in Figure 1, the abscissa x and the ordinatey denote the
comprehensive evaluation values of dimensionsIIES and EOLC,
respectively. Scopes of the abscissa andthe ordinate are the
continuous closed interval from DL toDH, and they take the abscissa
𝑥 = 𝑀 and the ordinate𝑦 = 𝑀 as the dividing lines. Thus, the whole
feasible regionin Figure 1 can be divided into four regions. If the
point
-
Mathematical Problems in Engineering 7
corresponding to the comprehensive evaluation values
ofdimensions IIES and EOLC is marked out in the figure, thenwe can
know which region the decision point of logisticsoperation mode
selection of the enterprise falls. Further,according to the Ballou
model shown in Figure 1, the suitablelogistics operation mode of
the enterprise can be selected.
5. Case Analysis
To illustrate the use of the above method, this section givesa
case analysis. Enterprise A from Inner Mongolia in Chinais a
manufacturer which produces “Mongolian hot pot soup”products; its
distributors can be found in more than twentyprovinces in China. In
selling process, enterprise A needsto deliver the products to the
distributors. To improve thequality of logistics service and ensure
that the productscan be delivered safely and efficiently to the
distributors,enterprise A needs to select the better logistics
operationmode. For this, the enterprise selects the experts from
therelated departments to form an expert committee. Theyinclude the
senior manager (𝐸1), the logistics manager (𝐸2),the sales manager
(𝐸3), the customer manager (𝐸4), and theoutside expert (𝐸5).
According to the method given above,the weight vector of the five
experts can be determined, i.e.,𝑊 = (𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5)𝑇 =
(H,DH,UH,M,M)𝑇.
According to the real situation of enterprise A, theevaluation
indexes in IIES dimension and EOLC dimensionare screened,
respectively.
According to themethod for the selection of the
languageevaluation set 𝑆, this expert groupwill be responsible to
all theevaluation work of enterprise A’s logistics operation
mode.
Firstly, the five experts give the fuzzy assessment matrixwith
respect to the weights of the six evaluation indices in
thedimension IIES, i.e.,
𝑈 = [𝑢𝑘𝑖]5×6 =[[[[[[[[[
DH M UH H DH UHUH DH DH UH M DHM M H UH UH DHUH L M UH DH UHUH L
UH H UH UH
]]]]]]]]]. (7)
Also, the five experts give the fuzzy assessment matrix
withrespect to the weights of the ten evaluation indices in
thedimension EOLC, i.e.,
𝑉 = [V𝑘𝑗]5×10
=[[[[[[[[[
UH UH L H H L UH UL M ULDH UH H DH M H H UH UH HUH H M H UH UH
DH L H UHUH UH M H UH H DH M DH DHDH DH UH UH UH M H M UH M
]]]]]]]]]. (8)
Then, according to the evaluation indices in dimensionsIIES and
EOLC, the five experts undertake evaluates of the
enterprise A and, respectively, determine the correspondingfuzzy
assessment matrices as follows:
𝑋 = [𝑥𝑘𝑖]5×6 =[[[[[[[[[
M UL M H M LDH L DH UH L DHL L M H UL MM M M UH DL HM L H M M
L
]]]]]]]]],
𝑌 = [𝑦𝑘𝑗]5×10
=[[[[[[[[[
M L UL M M H H UL UL LH UH M UL H UH UH M M MM M L M L M H M L
LL M L M M UL L L M DLM M M L M L H M L L
]]]]]]]]],
(9)
Further, the matrices 𝑈, 𝑉, 𝑋, and 𝑌 can be convertedinto the
2-tuple fuzzy linguistic form. On this basis, by (1)and (3), the
weights and the performance evaluation valuesof each expert with
respect to each index in the dimen-sion IIES can be obtained; i.e.,
the weights are (𝑢1, 𝛼𝑠1) =(UH, −0.29), (𝑢2, 𝛼𝑠2) = (H, −0.43),
(𝑢3, 𝛼𝑠3) = (UH, −0.24),(𝑢4, 𝛼𝑠4) = (UH, −0.33), (𝑢5, 𝛼𝑠5) = (UH,
−0.24), and(𝑢6, 𝛼𝑠6) = (DH, −0.48); the performance evaluation
valuesare (𝑥1, 𝛼𝑠1) = (H, −0.38), (𝑥2, 𝛼𝑠2) = (L, −0.05), (𝑥3, 𝛼𝑠3)
=(H, 0), (𝑥4, 𝛼𝑠4) = (H, 0.29), (𝑥5, 𝛼𝑠5) = (L, 0.19), and (𝑥6,
𝛼𝑠6)= (H, −0.33). In the same way, by (2) and (5), the weightsand
the performance evaluation values of each expert withrespect to
each index in the dimension EOLC can beobtained; i.e., the weights
are (V1, 𝛼𝑐1) = (UH, 0.43), (V2, 𝛼𝑐2)= (UH, −0.10), (V3, 𝛼𝑐3) = (M,
0.38), (V4, 𝛼𝑐4) = (UH, −0.29),(V5, 𝛼𝑐5) = (UH, −0.29), (V6, 𝛼𝑐6) =
(H, −0.29), (V7, 𝛼𝑐7) =(UH, 0.05), (V8, 𝛼𝑐8) = (M, −0.05), (V9,
𝛼𝑐9) = (UH, −0.48),and (V10, 𝛼𝑐10) = (H, −0.19); the performance
evaluationvalues are (𝑦1, 𝛼𝑐1) = (M, 0.14), (𝑦2, 𝛼𝑐2) = (M, 0.38),
(𝑦3, 𝛼𝑐3)= (L, 0.24), (𝑦4, 𝛼𝑐4) = (L, 0.29), (𝑦5, 𝛼𝑐5) = (M, 0.05),
(𝑦6, 𝛼𝑐6)= (M, 0.33), (𝑦7, 𝛼𝑐7) = (H, 0), (𝑦8, 𝛼𝑐8) = (L, 0.48),
(𝑦9, 𝛼𝑐9) =(L, 0.24), and (𝑦10, 𝛼𝑐10) = (L, 0).
Finally, by (4) and (6), we can obtain the
comprehensiveevaluation value of enterprise A the expert group,
i.e.,(𝑥, 𝛼𝑠) = (M, 0.29) and (𝑦, 𝛼𝑐) = (M, −0.13), which isshown as
the point P in region II in Figure 4. The point Pshows that the
importance of logistics to enterprise successA is higher, but the
enterprise operation logistics capacityis lower. According to the
two-dimensional decision matrixmodel shown in Figure 1, enterprise
Amay select the logisticsalliance mode and should need to seek the
strong partner inthe logistics alliance.
6. Conclusions
This paper presents a method for selecting the
enterpriselogistic operation mode based on the Ballou model.
Accord-ing to the Ballou model, the evaluation index sets of
two
-
8 Mathematical Problems in Engineering
IV I
III II
AH
M
AL AHM x
y
RL RH HL
L
RL
RH
H
[(M,0.29),(M,-0.13)]
P
Figure 4: The two-dimensional decision matrix of logistics
operat-ing mode selection.
dimensions IIES and EOLC for selecting enterprise logis-tics
operation mode are first determined through literatureanalysis.
Then, the method for selecting enterprise’s logisticsoperation mode
is given based on the fuzzy linguistic assess-ment method and the
2-tuple fuzzy linguistic representationmodel. Compared with the
existing methods, the proposedmethod has distinct characteristics
as discussed as follows.
First, the proposed method is based on the Ballou model.It has a
solid theoretical foundation.
Second, in the proposed method, the evaluation indexsets of two
dimensions IIES and EOLC for selecting enter-prise’s logistics
operation mode are based on literature anal-ysis. This is based on
a large number of existing researchresults.
Third, the proposedmethod is simple and easy to operate.It can
be used to guide the management practice of enter-prises.
It is necessary to point out that, for the different typesof
enterprises, the evaluation indexes and their weights maybe
different. In the practical application of the method, wecan adjust
the evaluation index sets according to the actualsituation of
enterprises.
In terms of future research, to facilitate better applicationof
the proposed method, the decision support system forselecting
enterprise logistics operation mode needs to bedeveloped.
Data Availability
The data used to support the findings of this study areincluded
within the article.
Conflicts of Interest
The authors declare that there are no conflicts of
interestregarding the publication of this paper.
Acknowledgments
This work was partly supported by the National NaturalScience
Foundation of China (Project no. 71871049) and the111 Project
(B16009).
References
[1] J. G. Nell, A Standardization Strategy at Matches
EnterpriseOperation, Springer, Berlin, Germany, 1997.
[2] P. Xing, C. H. Zhang, Y. L. Wang, and J. Jiang, “Logistics
servicequality control considering risk aversion under supply
disrup-tion,” Journal of Northeastern University (Natural Science),
vol.37, no. 4, pp. 604–608, 2016.
[3] A. S. Safaei, S. Farsad, and M. M. Paydar, “Robust bi-level
opti-mization of relief logistics operations,” Applied
MathematicalModelling, vol. 56, pp. 359–380, 2018.
[4] A. Gunasekaran, Z. Irani, K. Choy, L. Filippi, andT.
Papadopou-los, “Performance measures and metrics in outsourcing
deci-sions: A review for research and applications,”
InternationalJournal of Production Economics, vol. 161, pp.
153–166, 2015.
[5] H. Hsiao, R. Kemp, J. van der Vorst, and S. (Onno) Omta,“A
classification of logistic outsourcing levels and their impacton
service performance: Evidence from the food processingindustry,”
International Journal of Production Economics, vol.124, no. 1, pp.
75–86, 2010.
[6] T. Liao, “Reverse logistics network design for product
recoveryand remanufacturing,”AppliedMathematicalModelling, vol.
60,pp. 145–163, 2018.
[7] W. Ho, T. He, C. K. M. Lee, and A. Emrouznejad, “Strate-gic
logistics outsourcing: an integrated QFD and fuzzy AHPapproach,”
Expert Systems with Applications, vol. 39, no. 12, pp.10841–10850,
2012.
[8] D.-F. Li and S.-P. Wan, “Fuzzy heterogeneous
multiattributedecision making method for outsourcing provider
selection,”Expert Systems with Applications, vol. 41, no. 6, pp.
3047–3059,2014.
[9] K. Yao, Regional E-commerce enterprise logistics
distributionmode selection and process optimization design research
[Doc-toral, thesis], Nanjing University of Science and Technology
inChina, Nanjing, China, 2015.
[10] J. N. Su and C. F. Shi, “ISM-based impact factor analysis
inthe selection of logistics mode in manufacturing
enterprises,”Industrial Engineering Journal, vol. 12, no. 4, pp.
6–10, 2009.
[11] L. Cui and S.Hertz, “Networks and capabilities as
characteristicsof logistics firms,” IndustrialMarketingManagement,
vol. 40, no.6, pp. 1004–1011, 2011.
[12] Y. D. Gong and Q. L. Da, “Research on combinations of
closed-loop supply chain dominant mode and logistics mode,”
Journalof Management Sciences in China, vol. 18, no. 10, pp. 14–25,
2015.
[13] P. Yu, Research for logistics operation mode selection of
con-struction enterprises [Doctoral, thesis], Chongqing Jiao
tongUniversity in China, Chongqing, China, 2015.
[14] D. McFarlane, V. Giannikas, and W. Lu, “Intelligent
logistics:Involving the customer,”Computers in Industry, vol. 81,
pp. 105–115, 2016.
[15] A. Awasthi, “Evaluating new business operation models
forsmall and medium size logistics operators within low
emissionzones,” Transportation Research Procedia, vol. 12, pp.
707–717,2016.
-
Mathematical Problems in Engineering 9
[16] Y. F. Wang and W. Liu, “The blind number model of
evaluatingregional logistics capability under information chaos,”
ChineseJournal of Management, vol. 7, no. 3, pp. 418–422, 2010.
[17] R. Ballou, “Business logistics management: Planning,
organ-izing, and controlling the supply chain,” Instructors
Manu-aPrentice-Hall, pp. 37–40, 1999.
[18] W. Feng, “Enterprise benefit gamemodel of collaborative
supplychain in logistics industry park,” Journal of
ComputationalScience, vol. 27, pp. 469–475, 2018.
[19] Y. F. Wang, A synthetic research of bibliometric method
andcontent analysis method [Doctoral, thesis], Nanjing University
ofScience and Technology in China, Nanjing, China, 2007.
[20] F. Li, “A study on the forming mechanism of retailers’
logisticmodes,”Management Review, vol. 22, no. 10, pp. 14–25,
2010.
[21] Y. P. Zhao and L. Yan, “Study on the choice of logistics
deliverymodel in small and medium-sized manufacturing
enterprises,”Operations Research and Management Science, vol. 18,
no. 5, pp.163–167, 2009.
[22] L. Xu and S. Wang, “Empirical research on construct of
chainstore logistics capability system,” Ibusiness, vol. 04, no.
01, pp.10–17, 2012.
[23] W. Shen and S. H. Ma, “A scheduling model for
logisticscapability in a time-based multi-stage supply chain,”
Surveys inOperations Research and Management Science, vol. 16, no.
3, pp.20–25, 2007.
[24] F. Herrera and L.Mart́ınez, “A 2-tuple fuzzy linguistic
represen-tation model for computing with words,” IEEE Transactions
onFuzzy Systems, vol. 8, no. 6, pp. 746–752, 2000.
[25] F. Herrera and L. Mart́ınez, “A model based on linguistic
2-tuples for dealing with multi-granular hierarchical
linguisticcontexts in multi-expert decision-making,” IEEE
Transactionson Systems, Man, and Cybernetics, Part B (Cybernetics),
vol. 31,no. 2, pp. 227–234, 2001.
[26] L. A. Zadeh, “The concept of a linguistic variable and
itsapplication to approximate reasoning I,” Information
Sciences,vol. 8, pp. 199–249, 1975.
[27] L. Mart́ınez and F. Herrera, “An overview on the
2-tuplelinguistic model for computing with words in decision
making:extensions, applications and challenges,” Information
Sciences,vol. 207, pp. 1–18, 2012.
[28] Z. P. Fan, B. Feng, Y. H. Sun, andW. Ou, “Evaluating
knowledgemanagement capability of organizations: a fuzzy
linguisticmethod,” Expert Systems with Applications, vol. 36, no.
2, pp.3346–3354, 2009.
[29] Y. Liu, J.-W. Bi, andZ.-P. Fan, “Ranking products through
onlinereviews: A method based on sentiment analysis technique
andintuitionistic fuzzy set theory,” Information Fusion, vol. 36,
pp.149–161, 2017.
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