A Novel Intensive Distribution Logistics Network Design ...

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Research ArticleA Novel Intensive Distribution Logistics Network Design andProfit Allocation Problem considering Sharing Economy

Mi Gan ,1,2,3,4 Shuai Yang,1 Dandan Li,1 Mingfei Wang,1 Si Chen,1

Ronghui Xie,1 and Jiyang Liu1

1School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, China2Sino-US Global Logistics Institute, Shanghai Jiaotong University, Shanghai, China3National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University,Chengdu, Sichuan 610031, China4National Engineering Laboratory of Big Data Application in Integrated Transportation, Southwest Jiaotong University,Chengdu, Sichuan, China

Correspondence should be addressed to Mi Gan; [email protected]

Received 5 September 2017; Accepted 4 March 2018; Published 11 April 2018

Academic Editor: Carlos Gershenson

Copyright © 2018 Mi Gan 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 rapid growth of logistics distribution highlights the problems including the imperfect infrastructure of logistics distributionnetwork, the serious shortage of distribution capacity of each individual enterprise, and the high cost of distribution in China.While the development of sharing economy makes it possible to achieve the integration of whole social logistic resources, bigdata technology can grasp customer’s logistics demand accurately on the basis of analyzing the customer’s logistics distributionpreference, which contributes to the integration and optimization of the whole logistics resources. This paper proposes a kind ofintensive distribution logistics network considering sharing economy, which assumes that all the social logistics suppliers builda strategic alliance, and individual idle logistics resources are also used to deal with distribution needs. Analyzing customershopping behavior by the big data technology to determine customer’s logistics preference on the basis of dividing the customer’slogistics preference into high speed, low cost, and low pollution and then constructing the corresponding objective functionmodel according to different logistics preferences, we obtain the intensive distribution logistics network model and solve it withheuristic algorithm. Furthermore, this paper analyzes the mechanism of interest distribution of the participants in the distributionnetwork and puts forward an improved interval Shapley value method considering both satisfaction and contribution, with caseverifying the feasibility and effectiveness of the model. The results showed that, compared with the traditional Shapley method,distribution coefficient calculated by the improved model could be fairer, improve stakeholder satisfaction, and promote thesustainable development of the alliance as well.

1. Introduction

With the rapid development of e-commerce and online retailindustry, China’s logistics volume increased rapidly. Accord-ing to statistics, China’s express quantity reached 315.5 billionin 2016, nearly half of global express quantity and the expressindustry has grown by more than 50% over six consecutiveyears. Compared with the increasing demand of logistics,logistics distribution network and infrastructure lag behindobviously, which restricts the development of logistics, retail,and e-commerce. In order to solve the problem of insufficient

logistics resources, there are two main ideas. On the onehand, establishing more logistics facilities and, on the otherhand, integrating and optimizing the existing social logisticsresources. This paper focuses on the second point.

Sharing economy is a rapidly developed business modelsince 2010; it takes a variety of forms, including usinginformation technology to provide individuals with infor-mation that enables the optimization of resources throughthe mutualization of excess capacity in goods and services[1]. In recent years, the sharing economy has been wildlyused in many fields such as agriculture, finance, property,

HindawiComplexityVolume 2018, Article ID 4678358, 15 pageshttps://doi.org/10.1155/2018/4678358

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2 Complexity

CustomerLogistics Logistics

Private idle

providerLogistics

resources

Information

Big data platform

Storageplatform

Figure 1: Intensive distribution logistics network structure considering sharing economy.

transportation, and technology; there are also many attemptsto apply the sharing economy into logistics field. Some com-panies applied the sharing economy model to local delivery:Uber launched an app called Uber Eats which enables usersto register to be drivers and can get paid for delivering food[2]; Instacart applied sharing economy model to grocerydelivery by taking contract with workers who can use theirpersonal vehicles to deliver goods to customers [3, 4]. Sharingeconomy has also been wildly used in express industry; thelargest express alliance Cainiao in China aims to achieverational utilization of the whole society logistics resources byintegrating resources of seller, warehouse, and distributionbased on big data analysis and implementation rules. Withthe development of sharing economy, it is possible to integratethe whole society’s logistics resources. Meanwhile, the devel-opment of big data technology can provide technical supportfor strategic alliances. The express enterprises could makefull use of big data technology on the basis of constructingstrategic alliance and actively mobilize private idle logisticsresources of the society, which is predicted to be crucial toestablish the competitive advantage of the enterprise.

Logistics volume carried by distribution network of thewhole society is known as the units and residents items,whichis an important part of the total social logistics, includingthe baggage, parcels and letters in postal services, communitydonations, goods belonging to groups or residents whichneed to be transported, and logistics service items due tomoving house in railway and air transportation [5]. As thesource of more than 60% of the residents’ logistics demandis produced by the transaction of electronic commerce, bigdata by user transactions in e-commerce network could beconducive to further analysis and determination of logisticspreference of customers [6]. Customers log in electricitysupplier website, browse products, add to cart, give up thegoods or not, select the logistics distribution enterprises,

and so on, which would produce a huge user preferenceshopping behavior data, credit, and payment data. A certainmechanism to analyze and classify user preferences could beutilized, and the preferences could be met through targetedservices.

In terms of terminal distribution, the logistics distribu-tion terminals mainly cover the modes shown in Table 1 athome and abroad.

With the emergence of the sharing economy model,more andmore governments, enterprises, and scholars beginto consider how to make use of the private idle logis-tics capability to serve the social logistics and save thesocial cost. Intensive distribution logistics network struc-ture considering sharing economy could be represented asFigure 1. The design problem is analyzing and determin-ing the user logistics preferences through the electricitysupplier user data, which assumes that strategic allianceswould take full account of idle private logistics capabilityto canvass, provide terminal delivery service in logisticsfirst mile and last mile, and meet the needs of users aftersegmentation.

The logistics resources considered in this paper mainlyinclude logistics resources of third-party logistics companies,private idle resources of social logistics, and warehousingresources. At the same time, it takes into consideration thecapacity and characteristics of freight transportation by var-ious modes such as railway, highway, aviation, and shipping,to optimize the integration and logistics capabilities.

The constructions of strategic alliances and private logis-tics idle resources meet the demand of distribution together,which could not only greatly reduce the total cost of socialdistribution, but also improve customer satisfaction. Due toa certain degree of difference including resource input, risktaking, joining time, and effort of alliance between membersof the alliance, they should obtain different profits, and the

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Complexity 3

Table1:Major

logisticsd

istrib

utionterm

inalsm

odel.

Term

inalmod

eFo

reigncases

Dom

estic

cases

Streng

ths

Weakn

ess

Custo

mer

self-pickup

(CSP

)incommun

itysto

re

Dom

inated

byE-commerce

enterpris

es(e.g.,Argos)

Dom

inated

bylogistics

enterpris

es(e.g.,SF,H

eike)

(1)Reducingthes

torage

spacer

equirementsforg

oods

insto

res;

(2)increasin

gthee

xposureo

fcom

mod

ity;

(3)relatively

saving

theo

peratin

gcosts

ofsto

res;

(4)self-collectionisno

tbou

ndby

time.

(1)Thes

ervice

quality

ofcommun

itysto

resv

aries

greatly

;(2)th

eservice

coverage

isrelatively

narrow

;(3)a

need

forc

ultiv

atingtheh

abitof

custo

mer

self-pickup

.Self-pickup

service

pointsdo

minated

byE-commerce

orcourierc

ompany

DHLself-pickup

servicep

oints

CainiaoYizhan;

JDself-pickup

servicep

oints

(1)Solving

thep

roblem

ofmism

atch

betweendelivery

timea

ndpickup

time;

(2)reducingthed

eliverytim

eofcou

riers,achieving

high

quantitieso

fgoo

dsdistrib

ution.

Largeu

pfront

investm

ent,high

shop

costs

,and

labo

rcosts

.

Intelligent

parcel

deliverylocker

BufferBox

HiveB

oxtechno

logy;

ChinaP

ostP

arcel

Locker

(DBS

)

(1)Solving

thep

roblem

ofmism

atch

betweendelivery

timea

ndpickup

time;

(2)savingthew

aitin

gtim

eofp

arcelspickup

and

drop

-off;

(3)noneed

form

anualguards.

(1)Differentgoo

dsrequ

iredifferent

deliverycabinets;

(2)largeinvestm

entatthe

prelim

inarysta

geandhigh

maintenance

costs

later.

Collabo

ratio

nwith

conveniences

tores

RelayStar

JDconvenience

store

(1)Reducingtheu

pfront

investmentcostsof

e-bu

siness

logisticse

nterprise

s;(2)b

ringing

conveniences

toresw

ithconsiderable

traffi

cdue

toparcelsp

icku

p.

Blindspotse

xistingin

good

spreservationin

conveniences

tores,commod

ityinspectio

n,andqu

ality

mon

itorin

g;aft

er-saled

isputeisd

ifficultto

solve.

Sharingecon

omy

mod

elUberR

ush

DadaE

xpress;

Renren

Express

Afterb

uyersp

lacedan

orderthrou

ghmob

ilesm

art

term

inal,cou

riersin

thisservicea

reaw

illoff

ersm

all

parceldeliveryservices

throug

hprivatec

arsa

ndcharge

bydeliverydistance,w

hich

contrib

utes

tothe

prom

otionof

interactionbetweenindu

striesa

swellas

thed

eepening

ofintegrationof

commun

itydelivery

services.

Startin

glate,the

mod

eisrelatively

new,

needsto

cultivatethetrustbetweencommun

ityresid

ents;

indu

strysta

ndards

andregu

latio

nsareimperfe

ct.

Governm

ent

investingin

and

constructin

gcommun

itylogistics

publicfacilities

Tower

24plan

inDortm

und

(1)24-ho

urparcelsself-picku

pandself-drop

-off

services,quicklydeliveringwith

outgettin

goff

;(2)d

ivided

into

different

temperature

controlzon

es;

(3)autom

aticallysend

ingtext

messageso

re-m

ailsto

theb

uyer

after

service;

(4)paymentb

ytransfe

rorc

reditcard.

(1)Hugeu

pfront

investm

entcost;

(2)diffi

cultto

expand

ormoveo

ncec

onstructed.

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4 Complexity

scientific method of profit allocation is the key to ensure thestable operation of the strategic alliance.

The research of this paper is mainly divided into twoaspects. For one thing, assuming that the social idle logisticsresources can be fully employed under the strategic allianceof whole society logistic enterprises, then in order to meetthe different demands of consumers, a kind of intensivedistribution logistics network considering sharing economyis proposed and solved by heuristic algorithm. For another,in order to avoid the profit allocation conflict and the furtherthreat to the stability of the alliance, an improved intervalShapley value method taking both the satisfaction and thecontribution of alliance members into account is proposed topromote the sustainable development.

2. Literature Review

The design of distribution logistics network could be dividedinto multistage distribution and single stage distribution.The multistage distribution problem is usually solved as asingle stage transportation problem [7], and the single stagedelivery problem could also be regarded as the vehicle routingproblem [8]. In addition, the facility location problem isalso closely related to the design of the distribution logisticsnetwork [9]. Jansen et al. took the multiple traffic modeselection problem into the distribution problem [10]; Beamonsummarized the existing supply chain distribution problemsand divided them into two types: the quality model and thequantity model [11]; Gulpınar et al. studied facility locationand network design issues at the same time, aiming tominimize the maximum transport time from customers tofacilities [12]. Zarandi et al. investigated positioning trans-port route problem with time window under uncertainty(LRPTW). The authors assumed that customer demand andtravel time are fuzzy variables and established a fuzzy chanceconstrained programming (CCP) model using the credittheory, and the simulated annealing algorithmwas applied tosolve the problem [13]. Recent researches focus on consider-ing the distribution network design problem under differentsituations, for example, model construction considering thetraffic congestion [14], emergency condition [15], and envi-ronment constraints [16, 17].The solution algorithm is usuallybased on intelligent algorithm or heuristic algorithm, andthe performance and efficiency of the algorithm are relativelymature.

In terms of profit allocation of strategic alliances, scholarshave adopted quantitative analysis methods such as allianceShapley value [18], core [19], Weber set [20], nucleolus solu-tion [21], negotiation pricing method [22], and gamemethod[23]. As an important solution of the classical cooperativegame theory, the Shapley method has been widely appliedto solve the cooperative income distribution problem in theclassical cooperative game. However, the application of theclassical Shapley value method for profit allocation has somedefects. For example, when the real problem cannot meetthe assumptions of classical cooperative game, the classicalShapley valuemethod is no longer applicable; and the Shapleyvalue method takes the marginal contribution of the alliancemembers as the sole basis of profit allocation, ignoring

characteristic differences among members in the alliance,which has been questioned by scholars [24].

The first attempt to solve the symmetry problem wasmade by Shapley LS himself, who weakened the assumptionsabout the symmetry in Shapley value model, gave the mem-bers of the alliance the right weight, and constructed a moreextensive weighted Shapley value model, and the originalShapleymodel is only a special case [25]. Subsequently, Owenproposed the “diagonal formula” calculation for weightedShapley value [26, 27]. Kalai and Samet further extended the“weight” in weighted Shapley value model to the “weightingsystem,” that is, adding an ordered coalition group basedon the weight vector, which made the zero weight possibleand gave an axiomatic description of weighted Shapley valuein weight system [28]. At present, scholars mainly use theimproved Shapley value method to carry out the profitallocation in the supply chain [29–31] and the dynamicalliance [32, 33] scene. At the same time, to better solvethe problem of income allocation in practical cooperativegame, scholars at home and abroad have studied the problemof cooperative game under uncertain environment. Aubinfirst put forward the concept of fuzzy cooperative gameand defined a fuzzy number in the range [0, 1] to representthe degree of membership in a coalition, but the incomeof such fuzzy alliance is crisp real number [34]. Sakawaand Nishizaki proposed a cooperative strategy with clearalliance and fuzzy payoff functions [35]. Mares studied thefuzzy Shapley value of cooperative game and defined thefuzzy membership function of Shapley value, but failed togive the specific income distribution scheme of the alliance[36]. The Shapley value with fuzzy interval numbers takesthe uncertainties existing in the actual allocation processinto account, which could effectively solve the deficiency ofclassical Shapley values for accurate solutions.

Generally, there are many researches on the distributionnetwork design, but most emphasize the cost minimizationmodel by using the algorithm, which has its limitations.With the wave of sharing economic, this paper uses bigdata technology innovatively to segment customer logisticspreferences and construct intensive distribution logisticsnetwork considering sharing economy through differentlogistics preferences to guide the design and improvement oflogistics network and product. In terms of profit allocation,existing studies focused on the correction coefficient of distri-bution of Shapley, but the correction coefficient determinedcurrently has three main problems: (1) the considered factorsaffecting the cooperation profit distribution are single; (2) themost comprehensive correction coefficients are determinedby AHP and fuzzy evaluation method which show obvioussubjective tendency; (3) the importance of alliance membersfor profit satisfaction is ignored. In view of this, combinedwith the basic characteristics and operation mechanism ofexpress enterprise alliance, this paper proposes an improvedinterval Shapley value method considering both satisfactionand contribution to modify the distribution coefficient valuemethod, which obtains a fairer and more reasonable profitallocation result.

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Complexity 5

Logistics providerCustomer Customer

Private idleresources

Logistics provider

Private idleresources

Strategicalliances

Place order

payment

Inform

Canvassing

Goodscollection Transportation Inform

Originwarehouse

Destinationwarehouse

Terminaldistribution

① ② ③ ④ ⑤ ⑥

Figure 2: Conceptual model considering sharing economy.

3. Intensive Distribution Logistics NetworkDesign Model considering Sharing Economy

3.1. Basic Instructions, Marking, and Assumptions. Throughthe analysis of the distribution network, the logistics net-work is divided into six parts: receiving order, canvassing,collecting goods to warehouse, transportation to destinationwarehouse, warehouse delivery, and terminal distribution, asshown in Figure 2.

Decisions on the design of this distribution network needto be made, including the following:

(1) When the enterprise receives orders, the allianceconfirms and arranges logistics service providers orprivate idle resources to canvass immediately.

(2) Select original warehouse after canvass.(3) Select transportation provider for transportation.(4) Select appropriate destination warehouse after trans-

portation.(5) Choose appropriate terminal distributor.(6) Handle terminal distribution.

At the same time, in order to facilitate the establishmentof the model, according to the actual situation in reality, thefollowing basic assumptions are made:

(1) Each customer has only one logistics preference, thatis, shortest transport time, lowest cost, or lowestcarbon emissions.

(2) The third-party logistics providers of the wholesociety and private construct strategic alliances andintegrate, optimize, and configure resources.

(3) Solicitation fee of the third-party logistics distributionincludes pickup and delivery to the original ware-house; terminal distribution costs of the third-partylogistics providers include picking up goods from thearrival warehouse and delivering them to the finalcustomer correspondingly.

(4) The private could use idle logistics resources toprovide canvass and terminal delivery service, and theincome is paid by the strategic alliance.

The description of basic parameters is as follows:

𝐷 = {𝑑1, 𝑑2, . . . , 𝑑𝑑}: consignors;𝐹 = {𝑓1, 𝑓2, . . . , 𝑓𝑓}: the third-party logistics providerat the origin;

𝐺 = {𝑔1, 𝑔2, . . . , 𝑔𝑔}: the aggregation of freightcollection warehouse at the origin;𝐾 = {𝑘1, 𝑘2, . . . , 𝑘𝑘}: the aggregation of freightcollection warehouse at the destination;𝐿 = {𝑙1, 𝑙2, . . . , 𝑙𝑙}: the third-party logistics provider atthe destination;𝐶 = {𝑐1, 𝑐2, . . . , 𝑐𝑐}: aggregation of all end-customers;𝑃 = {𝑝1, 𝑝2, . . . , 𝑝𝑝}: aggregation of third-partylogistics providers from the original warehouse to thedestination warehouse;𝑒 = {𝑒1, 𝑒2, 𝑒3}: three types of customer logisticspreferences based on big data analysis; 𝑒1, 𝑒2, 𝑒3 repre-sent shortest transport time, lowest logistics cost, andlowest carbon emissions individually;𝑥𝑑𝑓, 𝑦𝑓𝑔, 𝑧𝑝𝑔𝑘, 𝛿𝑘𝑙, 𝜀𝑙𝑐 are all 0-1 variables, 𝑥𝑑𝑓 isequal to 1 when a third-party logistics provider in𝐹 canvasses goods delivered from consignor of 𝐷;𝑦𝑓𝑔 equals 1 when any of the third-party logisticsproviders in 𝐹 canvasses goods and delivers them tooriginal warehouse which belongs to 𝐺; 𝑧𝑝

𝑔𝑘takes 1

when company in 𝑃 undertakes transport between 𝑔and 𝑘; 𝜀𝑙𝑐 equals 1 when selection 𝑙𝑖 in 𝐿 delivers goodsto terminal end-customer belonging to 𝐶;𝑞𝑑𝑓, 𝑞𝑓𝑔, 𝑞𝑘𝑙, 𝑞𝑙𝑐: logistics volume of 𝑑−𝑓, 𝑓−𝑔, 𝑔−𝑘,𝑙 − 𝑐, respectively;𝜃𝑝𝑔𝑘: the transport volume of the third-party logistics

provider 𝑃 from original warehouse 𝐺 to destinationwarehouse𝐾;𝑄𝑔, 𝑄𝑘: handling capability limits between originalwarehouses 𝐺 and destination warehouses𝐾, respec-tively;𝑄𝑓, 𝑄𝑃, 𝑄𝑙: logistics capabilities limit of logisticsprovider set 𝐹, 𝑃, 𝐿, respectively;𝑞𝑑, 𝑞𝑐: consignor’s delivering amount and receiver’srequirements;𝑡𝑑𝑓, 𝑡𝑔, 𝑡𝑝𝑔𝑘, 𝑡𝑘, 𝑡𝑙𝑐: canvassing time, handling timein original warehouses, transport time from originalwarehouse to destination warehouse, handling timein destination warehouses, and terminal distributiontime;V𝑑𝑓, ℎ𝑔, 𝑐𝑝𝑔𝑘, 𝑑𝑘, 𝑟𝑙𝑐: canvassing fee of unit goods,handling fee in original warehouse, transportationfees, handling fees in destination warehouse, andterminal distribution costs;

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6 Complexity

InformationLogistics

f

f

f

f

f

f

d

d

d

g

g

g

c

c

c

l

l

l

k

k

k

p

p

Figure 3: Network model considering sharing economy.

𝑐𝑜𝑑𝑓, 𝑐𝑜𝑝𝑔𝑘, 𝑐𝑜𝑙𝑐: carbon emissions in process of can-vassing of unit goods, transportation, and terminaldistribution costs due to vehicles operation;

The network model considering sharing economy couldbe obtained according to conceptual model and basic param-eters, as shown in Figure 3.

3.2. Multiobjective Programming Model

3.2.1. Customer Logistics Preference Objective FunctionBased on Big Data Analysis

(1) Shortest Transport Time. Such customers mainly pursuetimeliness when shopping in the electricity supplier website.The objective function could be therefore constructed tominimize the overall delivery time, as follows:

min 𝑍𝑒1= 𝑑𝑑∑𝑑1

𝑓𝑓∑𝑓1

𝑥𝑑𝑓𝑡𝑑𝑓 + 𝑓𝑓∑𝑓1

𝑔𝑔∑𝑔1

𝑦𝑓𝑔𝑡𝑓𝑔 + 𝑔𝑔∑𝑔1

𝑘𝑘∑𝑘1

𝑝𝑝∑𝑝1

𝑧𝑝𝑔𝑘𝑡𝑝𝑔𝑘

+ 𝑘𝑘∑𝑘1

𝑙𝑙∑𝑙1

𝛿𝑘𝑙𝑡𝑘𝑙 + 𝑙𝑙∑𝑙1

𝑐𝑐∑𝑐1

𝜀𝑙𝑐𝑡𝑙𝑐.(1)

(2) Lowest Logistics Cost. The goal of this kind of customeris consistent with the goal of the general logistics networkplanning problem, which requires the lowest overall logisticscost, so the corresponding delivery logistics price is relativelylow. The objective function can be constructed to minimizethe overall distribution costs, as follows:

min 𝑍𝑒2= 𝑑𝑑∑𝑑1

𝑓𝑓∑𝑓1

𝑞𝑑𝑓V𝑑𝑓 + 𝑓𝑓∑𝑓1

𝑔𝑔∑𝑔1

𝑞𝑓𝑔ℎ𝑓𝑔 + 𝑔𝑔∑𝑔1

𝑘𝑘∑𝑘1

𝑝𝑝∑𝑝1

𝑞𝑝𝑔𝑘𝑐𝑝𝑔𝑘

+ 𝑘𝑘∑𝑘1

𝑙𝑙∑𝑙1

𝑞𝑘𝑙𝑑𝑘𝑙 + 𝑙𝑙∑𝑙1

𝑐𝑐∑𝑐1

𝑞𝑙𝑐𝑟𝑙𝑐.(2)

(3) Lowest Carbon Emissions. Such customers are concernedabout carbon emissions during the distribution process, andthey are willing to undertake environmental responsibilityregardless of costs. This paper only considers the carbonemissions links including canvass, transportation, and finaldistribution, ignoring warehouse processing. The objectivefunction is the minimum carbon emissions in the process ofthe entire distribution, which is as follows:

min 𝑍𝑒3= 𝑑𝑑∑𝑑1

𝑓𝑓∑𝑓1

𝐶𝑂𝑑𝑓𝑞𝑑𝑓 + 𝑔𝑔∑𝑔1

𝑘𝑘∑𝑘1

𝑝𝑝∑𝑝1

𝐶𝑂𝑝𝑔𝑘𝑞𝑝𝑔𝑘

+ 𝑙𝑙∑𝑙1

𝑐𝑐∑𝑐1

𝐶𝑂𝑙𝑐𝑞𝑙𝑐.(3)

3.2.2. Constraints on the Design of Distribution Logistics Net-work. According to flow conservation of logistics node, flowconservation of transport volume and arrival quantity, logis-tics capability limitation of third-party logistics providers,warehouse capacity limitation, overall distribution processtime limitation, and the constraints of each decision variable’sown attributes and relationships, the following constraintscould be obtained:

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Complexity 7

St: ∑𝑑

𝑞𝑑𝑓𝑥𝑑𝑓 ≤ 𝑄𝑓, 𝑓 ∈ 𝐹, 𝑑 ∈ 𝐷, (4)

∑𝑑

𝑞𝑑𝑓𝑥𝑑𝑓 = ∑𝑓

𝑞𝑓𝑔𝑦𝑓𝑔, 𝑑 ∈ 𝐷, 𝑓 ∈ 𝐹, 𝑔 ∈ 𝐺, (5)

∑𝑓

𝑞𝑓𝑔𝑦𝑓𝑔 ≤ 𝑄𝑔, 𝑓 ∈ 𝐹, 𝑔 ∈ 𝐺, (6)

∑𝑓

𝑞𝑓𝑔𝑦𝑓𝑔 = ∑𝑘

∑𝑝

𝜃𝑝𝑔𝑘𝑧𝑝𝑔𝑘,

𝑓 ∈ 𝐹, 𝑔 ∈ 𝐺, 𝑘 ∈ 𝐾, 𝑝 ∈ 𝑃, (7)

∑𝑘

∑𝑝

𝜃𝑝𝑔𝑘𝑧𝑝𝑔𝑘≤ 𝑄𝑔, 𝑔 ∈ 𝐺, 𝑘 ∈ 𝐾, 𝑝 ∈ 𝑃, (8)

∑𝑘

∑𝑝

𝜃𝑝𝑔𝑘𝑧𝑝𝑔𝑘≤ 𝑄𝑔, 𝑔 ∈ 𝐺, 𝑘 ∈ 𝐾, 𝑝 ∈ 𝑃, (9)

∑𝑔

∑𝑝

𝜃𝑝𝑔𝑘𝑧𝑝𝑔𝑘= ∑𝑙

𝑞𝑘𝑙𝛿𝑘𝑙,𝑔 ∈ 𝐺, 𝑘 ∈ 𝐾, 𝑝 ∈ 𝑃, 𝑙 ∈ 𝐿, (10)

∑𝑙

𝑞𝑘𝑙𝛿𝑘𝑙 ≤ 𝑄𝑘, 𝑘 ∈ 𝐾, 𝑙 ∈ 𝐿, (11)

∑𝑘

𝑞𝑘𝑙𝛿𝑘𝑙 ≤ 𝑄𝑙, 𝑘 ∈ 𝐾, 𝑙 ∈ 𝐿, (12)

∑𝑙

𝑞𝑙𝑐𝛿𝑙𝑐 = 𝑞𝑐, 𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿, (13)

∑𝑐

𝑞𝑐 = ∑𝑑

𝑞𝑑, 𝑐 ∈ 𝐶, 𝑑 ∈ 𝐷, (14)

𝑥𝑑𝑓 = 𝑦𝑓𝑔, ∀𝑓 ∈ 𝐹, (15)

𝑦𝑓𝑔 = 𝑧𝑝𝑔𝑘, ∀𝑔 ∈ 𝐺, (16)

𝑧𝑝𝑔𝑘= 𝛿𝑘𝑙, ∀𝑘 ∈ 𝐾, (17)

𝛿𝑘𝑙 = 𝜀𝑙𝑐, ∀𝑙 ∈ 𝐿, (18)

𝑥𝑑𝑓, 𝑦𝑓𝑔, 𝑧𝑝𝑔𝑘, 𝛿𝑘𝑙, 𝜀𝑙𝑐 = {0, 1}𝑑 ∈ 𝐷, 𝑓 ∈ 𝐹, 𝑔 ∈ 𝐺, 𝑘 ∈ 𝐾, 𝑝 ∈ 𝑃, 𝑙 ∈ 𝐿, 𝑐 ∈ 𝐶, (19)

𝑞𝑑𝑓, 𝑞𝑓𝑔, 𝑞𝑘𝑙, 𝑞𝑙𝑐, 𝜃𝑝𝑗𝑘 ≥ 0𝑑 ∈ 𝐷, 𝑓 ∈ 𝐹, 𝑔 ∈ 𝐺, 𝑘 ∈ 𝐾, 𝑝 ∈ 𝑃, 𝑙 ∈ 𝐿, 𝑐 ∈ 𝐶. (20)

Constraint (4) is the logistics ability limitation of a third-party logistics company canvass that the consignor utilizes;Constraint (5) represents conservation of total quantitybetween canvassing of the third-party logistics company andits sending to original warehouse; Constraint (6) representsthe handling capacity limit of original warehouse canvasscapacity; Constraint (7) represents the flow conservation oforiginal warehouse; Constraint (8) represents the handlingcapacity limitation of the transmission capacity of originalwarehouse; Constraint (9) represents the capacity quantity

handling limitation of the destination warehouse; Constraint(10) represents the flow conservation of destination ware-house; Constraint (11) represents the handling capacity limi-tation of the transmission capacity of destination warehouse;Constraint (12) represents the terminal distribution capabilitylimitation of the third-party logistics company; Constraint(13) represents conservation of the total amount of customerreceipt and its demand; Constraint (14) represents flowconservation between sending quantity of origin and receivedquantity of destination; Constraints (15)–(18) represent acommitment relationship between 0-1 variables to help indecisionmaking; Constraints (19) and (20) are 0, 1 constraintsand nonnegative constraints for decision variables.

3.3. Model Transformation Mechanism and Algorithm.Through big data analysis of the user’s historical behavior,we could get the proportion of the customers of all kinds oflogistics preferences in the total customers and deal with theobjective functions (1)–(3).

3.3.1. Normalization Processing. Since the units of differentobjective functions are different, it is necessary to handlethe values in (1)–(3) by the normalization method. Vectornormalization method is used here as follows:

𝑧−𝑒1 = 𝑧𝑒1√𝑧𝑒12 + 𝑧𝑒22 + 𝑧𝑒32 ,𝑧−𝑒2 = 𝑧𝑒2√𝑧𝑒12 + 𝑧𝑒22 + 𝑧𝑒32 ,𝑧−𝑒3 = 𝑧𝑒3√𝑧𝑒12 + 𝑧𝑒22 + 𝑧𝑒32 .

(21)

3.3.2. Handing by the Linear Weighted Sum Method. Byobserving the objective function, it can be seen that theobjective functions (1), (2), (3) are the minimization of theobjective function, and the total objective function value ofthe distribution network is obtained by the linear weightedsum method, and the total model is as follows:

model DLND: min 𝑍 = 𝛼𝑧−𝑒1 + 𝛽𝑧−𝑒2 + 𝛾𝑧−𝑒3constraints: (4)–(20) . (22)

3.3.3. Model Solving Algorithm. It could be observed that theDLNDmodel is a classic mixed integer programming model.Branch and bound method, cutting plane method, or heuris-tic algorithm and genetic algorithm, simulated annealingalgorithm, and other intelligent algorithms could solve thiskind of problem effectively. In addition, various optimizationsoftware tools havemoremature toolboxes, such asMATLABwhich has been used to solve such problems. For practicalproblems, the above method could be used to solve theproblem effectively.

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4. Analysis of Members’ Profit Allocation inIntensive Distribution Logistics Network

This section analyzes benefits allocation in the distributionnetwork and proposes an improved Shapley value methodwhich considers both contribution and satisfaction.

4.1. Interval Shapley Value Method. The interval Shapleyfunction is consistent with the classical Shapley function inthe form, but the former is the natural expansion of thelatter under the fuzzy information condition.Thedistributionquota of its participants is as follows:

𝑥𝑖 = ∑ (𝑠 − 1)! (𝑛 − 𝑠)!𝑛! [V (𝑆) − V (𝑆 − {𝑖})] , (23)

where 𝑖 = (1, 2, . . . , 𝑛) represents participants in economicactivity, 𝑛 is the number of participants, and𝑁 = {1, 2, . . . , 𝑛}is a set of participants; 𝑆 is all subsets which include 𝑖belonging to 𝑁; 𝑆 is the number of subset members; themarginal contribution V(𝑠) − V(𝑠 − {𝑖}) could be allocatedto participant 𝑖 when this participant joins the coalition.Shapley value model can be regarded as a completely randomprocedure for all participants by the average expected payoff.

However, the allocation of league members 𝑖 is no longera definite value in this paper. It is an interval number that canbe expressed as [𝑥−𝑖 , 𝑥+𝑖 ], where 𝑥−𝑖 and 𝑥+𝑖 are the lower andupper bounds of the allocation of 𝑖.

𝑥−𝑖 = ∑ (𝑠 − 1)! (𝑛 − 𝑠)!𝑛! [V− (𝑆) − V− (𝑆 − {𝑖})] ,𝑥+𝑖 = ∑ (𝑠 − 1)! (𝑛 − 𝑠)!𝑛! [V+ (𝑆) − V+ (𝑆 − {𝑖})] . (24)

In particular, the subtraction of Shapley interval numbersneeds to be explained. The interval number subtractioncan be regarded as the inverse operation of adding intervalnumbers [37].

Assuming that 𝐼(𝑅) represents a set of all bounded closedintervals on 𝑅, 𝐼, 𝐽 ∈ 𝐼(𝑅) and 𝐼 = [𝐼−, 𝐼+], 𝐽 = [𝐽−, 𝐽+], thenthe subtraction of interval numbers can be defined as 𝐼Θ𝐽 =[𝐼− − 𝐽−, 𝐼+ − 𝐽+], if and only if 𝐼− − 𝐽− ≥ 𝐼+ − 𝐽+.4.2. Measuring Method of Factors and Coefficients AffectingAlliance Profit Distribution. Whether the surplus benefitdistribution mechanism is reasonable and perfect is the keyto the successful operation of the strategic alliance. Since theconstruction of strategic alliances is a contractual model, therights, obligations, and responsibilities of each stakeholderneed to be clearly defined. Therefore, the allocation of profitsshould consider the resources input, risk sharing, the timeto join the alliance, the satisfaction of the members of thealliance, the level of efforts, and so on. If the profits of logisticenterprises are not increased after the alliance, there is nopoint in forming the alliance, so this paper assumes that theprofits of logistic companies after joining the alliance are notless than before. For the effective implementation of the profitdistribution rules, we assume that members cannot join orwithdraw from the alliance at any time; that is, the alliancereceives or eliminates members on a regular basis.

(1) Resource Input. It mainly includes fixed assets, humanresources, innovation resources, and other intangible assets.What needs to be explained is the intangible asset. Forall enterprises, intangible assets are the brand value of theenterprise, which is also the enterprise image in the mindsof customers.

(2) Risk Sharing and Role Positioning.After the establishmentof the alliance, alliance members undertake different rolesand division of labor in the league, so the risk each memberundertakes is inconsistent with their role positioning. Mem-bers who take higher risks and more complex works shouldget the higher profit distribution.

(3) Effort Level.The level of effort is reflected in the quantityand quality of the work done by alliance members in actualoperation of the alliance. It mainly constrains the unilateralnegative working behavior of members in the alliance andensures that overall interests of the alliance are not compro-mised. The indicators can be obtained through assessing thework of alliance members.

(4) Satisfaction of Alliance Members.Most existing researchesignore the importance of alliance members’ satisfaction tothe overall profit allocation. It has been proved that highersatisfaction could stimulate the enthusiasm of alliance mem-bers and the stability of alliance structure. The satisfactioncoefficient could be obtained through questionnaire surveys,interviews, and other methods in the group.

4.3. Multiple Attribute DecisionMakingModel of IntuitionisticFuzzy Sets Based on TOPSIS. TOPSIS (Technique for OrderPreference by Similarity to Idea Solution) is a rankingmethodto ideal solution, which was first proposed by Hwang andYoon. It has been widely used in multi-index evaluationin recent years [38–41]. The basic idea is that the chosensatisfactory scheme is as close as possible to the positiveideal solution (or scheme) and away from the negative idealsolution as far as possible.

Step 1 (construct the intuitionistic fuzzy sets decisionmatrix).Supposing 𝐴 = (𝑎1, 𝑎2, . . . , 𝑎𝑛) is the 𝑛 enterprises of thewhole society logistics enterprise, 𝐵 = (𝑏1, 𝑏2, . . . , 𝑏𝑛) is theevaluation index set about enterprise.The intuitionistic fuzzynumber 𝑥𝑖𝑗 = (⟨𝑢𝑖𝑗 V𝑖𝑗⟩) represents the evaluation value of the𝑗 index of enterprise 𝑎𝑖 where 𝑖 = 1, 2, . . . , 𝑚, 𝑗 = 1, 2, . . . , 𝑛;0 ≤ 𝑢𝑖𝑗 + V𝑖𝑗 ≤ 1; 𝑢𝑖𝑗 ∈ [0, 1], V𝑖𝑗 ∈ [0, 1], respectively,represent the degree of consistency and inconsistency of theevaluation value of the evaluation index 𝑏𝑗 of the enterprise𝑎𝑖. 𝜋𝑖𝑗 = 1 − 𝑢𝑖𝑗 − V𝑖𝑗 is the hesitation degree of the enterprise𝑎𝑖 on the evaluation index 𝑏𝑗, and 0 ≤ 𝑢𝑖𝑗 + V𝑖𝑗 ≤ 1.

The intuitionistic fuzzy set multiple attribute decisionmaking problem could be expressed as a matrix:

𝑋 = (𝑥𝑖𝑗)𝑚×𝑛 . (25)

Step 2 (determine weights of evaluation indexes by usingtrapezoidal fuzzy number). There are many methods todetermine the weight of evaluation indexes, such as AHP

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(Analytic Hierarchy Process), factor analysis, informationentropy, attributing weight, and ordered chain method [42].However, the abovemethods have the disadvantages of strongsubjectivity, complex algorithm, and large randomness. Nehiand Maleki (2005) [43] proposed intuitionistic trapezoidalfuzzy numbers and some operators for them, which extendeddiscrete set to continuous set. The advantages of this methodinclude the following: (1) it could reflect the fuzziness anduncertainty of expert judgment; (2) without a large amountof data, the calculation method is simple and the judgmentmatrix is not needed.Therefore, in this paper, the trapezoidalfuzzy number is used to determine the weight of index set.

In the actual decision making, the weights of each indexare difficult to accurately determine, which are in a fuzzyinterval. It is therefore supposed in this paper that 𝜔𝑖 =(⟨𝛼𝑖, 𝛽𝑖⟩) represents the weight corresponding to the index 𝑏𝑗,in which, 0 ≤ 𝛼𝑖, 𝛽𝑖 ≤ 1 indicate the degree of importanceand unimportance of the evaluation index 𝑏𝑗, respectively,0 ≤ 𝛼𝑖 + 𝛽𝑖 ≤ 1; all the evaluation indexes could be expressedas intuitionistic fuzzy set vectors:

𝑤 = (𝜔1, 𝜔2, . . . , 𝜔𝑚)𝑇= (⟨𝛼1, 𝛽1⟩ , ⟨𝛼2, 𝛽2⟩ , . . . , ⟨𝛼𝑚, 𝛽𝑚⟩)𝑇 . (26)

According to the definition of intuitionistic trapezoidalfuzzy number, an intuitionistic trapezoidal fuzzy number 𝐴with parameters 𝑏1 ≤ 𝑎1 ≤ 𝑏2 ≤ 𝑎2 ≤ 𝑎3 ≤ 𝑏3 ≤ 𝑎4 ≤ 𝑏4is denoted as 𝐴 = ⟨(𝑎1, 𝑎2, 𝑎3, 𝑎4), (𝑏1, 𝑏2, 𝑏3, 𝑏4)⟩ in the set ofreal number 𝑅. In this case, the membership function andnonmembership function can be given as

𝛼 (𝑥) ={{{{{{{{{{{{{{{{{{{{{{{

0, 𝑥 < 𝑎1𝑥 − 𝑎1𝑎2 − 𝑎1 , 𝑎1 ≤ 𝑥 ≤ 𝑎21, 𝑎2 ≤ 𝑥 ≤ 𝑎3𝑥 − 𝑎4𝑎3 − 𝑎4 , 𝑎3 ≤ 𝑥 ≤ 𝑎40, 𝑎4 ≤ 𝑥,

𝛽 (𝑥) ={{{{{{{{{{{{{{{{{{{{{{{{{

1, 𝑥 < 𝑏1𝑥 − 𝑏2𝑏1 − 𝑏2 , 𝑏1 ≤ 𝑥 ≤ 𝑏20, 𝑏2 ≤ 𝑥 ≤ 𝑏3𝑥 − 𝑏3𝑏4 − 𝑏3 , 𝑏3 ≤ 𝑥 ≤ 𝑏41, 𝑏4 ≤ 𝑥.

(27)

The weighted intuitionistic fuzzy set decision matrix isformulated as follows:

𝑅 = (𝑟𝑖𝑗)𝑚×𝑛 , (28)

in which

𝑟𝑖𝑗 = 𝜔𝑗𝑥𝑖𝑗 = ⟨𝛼𝑗, 𝛽𝑗⟩ ⟨𝑢𝑖𝑗, V𝑖𝑗⟩= ⟨𝛼𝑗𝑢𝑖𝑗, 𝛽𝑗 + V𝑖𝑗 − 𝛽𝑖V𝑖𝑗⟩ = ⟨𝑢𝑖𝑗, V𝑖𝑗⟩ . (29)

Step 3 (determine intuitionistic fuzzy positive and negativeideal solutions). Suppose that positive ideal solutions𝐴+ andnegative ideal solutions 𝐴− of the intuitionistic fuzzy setscould be, respectively, expressed as

𝐴+ = (𝑟+1 , 𝑟+2 , . . . , 𝑟+𝑚)𝑇 ,𝐴− = (𝑟−1 , 𝑟−2 , . . . , 𝑟−𝑚)𝑇 , (30)

in which, 𝑟+𝑗 = ⟨𝑢+𝑗 , V+𝑗 ⟩, 𝑟−𝑗 = ⟨𝑢−𝑗 , V−𝑗 ⟩.𝑟+𝑗 = {{{

𝑢+𝑗 = max1≤𝑖≤𝑚

𝑢𝑖𝑗V+𝑗 = min1≤𝑖≤𝑚

V𝑖𝑗, (31)

𝑟+𝑖 = {{{𝑢−𝑗 = min1≤𝑖≤𝑚

𝑢𝑖𝑗V−𝑗 = max1≤𝑖≤𝑚

V𝑖𝑗, (32)

in which, 𝑟+𝑗 = ⟨𝑢+𝑗 , V+𝑗 ⟩, 𝑟−𝑗 = ⟨𝑢−𝑗 , V−𝑗 ⟩ are all intuitionisticfuzzy values, 0 ≤ 𝑢+𝑗 + V+𝑗 ≤ 1, 0 ≤ 𝑢−𝑗 + V−𝑗 ≤ 1.Step 4 (computing distance). The distance between the enter-prise 𝑎𝑖 and the positive and negative ideal solution is definedas 𝑑(𝑎𝑖, 𝐴+), 𝑑(𝑎𝑖, 𝐴−), expressed as follows:

𝑑 (𝑎𝑖, 𝐴+)= √ 12

𝑛∑𝑗=1

[(𝑢𝑖𝑗 − 𝑢+𝑗 )2 + (V𝑖𝑗 − V+𝑗 )2 + (𝜋𝑖𝑗 − 𝜋+𝑗 )2],𝑖 = 1, 2, . . . , 𝑚,

𝑑 (𝑎𝑖, 𝐴−)= √ 12

𝑛∑𝑗=1

[(𝑢𝑖𝑗 − 𝑢−𝑗 )2 + (V𝑖𝑗 − 𝑢−𝑗 )2 + (𝜋𝑖𝑗 − 𝜋−𝑗 )2],𝑖 = 1, 2, . . . , 𝑚,

(33)

in which, 𝜋𝑖𝑗 = 1−𝑢𝑖𝑗 − V𝑖𝑗; 𝜋+𝑗 = 1−𝑢+𝑗 − V+𝑗 ; 𝜋−𝑗 = 1−𝑢−𝑗 − V−𝑗 .Step 5 (calculation of relative closeness and contribution).The calculation of relative closeness between the enterprise𝑎𝑖 and the positive ideal solution of 𝐴+ is shown as follows:

𝜆𝑖 = 𝑑 (𝑥𝑖, 𝐴−)𝑑 (𝑥𝑖, 𝐴−) + 𝑑 (𝑥𝑖, 𝐴+) . (34)

Obviously, 0 ≤ 𝜆𝑖 ≤ 1, and the larger the 𝜃𝑖 is, the higherthe overall evaluation of the attributes of the correspondingenterprise 𝑎𝑖 is. To normalize the 𝜆𝑖, the contribution rate ofenterprise 𝑎𝑖 in the alliance operation is 𝜃𝑖:

𝜃𝑖 = 𝜆𝑖∑𝑚𝑖=1 𝜆𝑖 , (35)

in which∑𝑚𝑖=1 𝜃𝑖 = 1, and the greater the 𝜃𝑖 is, themore profitsthe enterprises 𝑎𝑖 should be allocated.

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Step 6 (determination of correction coefficients). The inter-val Shapley value method holds that the logistics enterprisesshould bear the resource input, risk sharing, and effort atequal level (i.e., 1/𝑛) when setting up strategic alliances.However, this is not the actual case, so the benefit allocationcorrection factor is amended as

Δ𝜃𝑖 = 𝜃𝑖 − 1𝑛 , 𝑖 = 1, 2, . . . , 𝑚,𝑚∑𝑖=1

Δ𝜃𝑖 = 0. (36)

4.4. Improvements considering Stakeholder Satisfaction. Inthe above section, the profit distribution coefficient of thestrategic alliance members has been adjusted according tothe actual resource input, the risk taking, and the effort levelof the member enterprises. To study the impact of alliancemembers’ satisfaction on overall profit distribution to thestructural stability of the strategic alliance, considering theasymmetry of stakeholder satisfaction, the Nash negotiationmodel is used to adjust the profit distribution scheme.First, determine the stakeholder’s satisfaction coefficient tothe initial benefit allocation scheme. If stakeholder 𝑖 is notsatisfied with the initial benefit allocation factor, stakeholder𝑖 would propose the benefit allocation scheme as

ℎ𝑖 = (ℎ1𝑖, ℎ2𝑖, . . . , ℎ𝑛𝑖)𝑇 𝑖 = 1, 2, . . . , 𝑚, (37)

where ℎ𝑗𝑖 represents the benefit allocation factor of thestrategic alliance member 𝑗 proposed by the stakeholder 𝑖,0 ≤ ℎ𝑗𝑖 ≤ 1, and ∑𝑛𝑗=1 ℎ𝑗𝑖 = 1, 𝑖 = 1, 2, . . . , 𝑚. The followingdiscussion shows how to revise the profit distribution schemeproposed by the members of the strategic alliance and howthe members of the strategic alliance negotiate and obtaina profit distribution coefficient 𝜑∗ = (𝜑∗1 , 𝜑∗2 , . . . , 𝜑∗𝑚) and∑𝑚𝑖=1 𝜑∗𝑖 = 1 that is satisfactory to all members.

Suppose that the ideal profit distribution scheme of thestrategic alliance members is

ℎ+ = {ℎ+1 , ℎ+2 , . . . , ℎ+𝑛 } , 𝑚∑𝑖=1

ℎ+𝑖 ≥ 1. (38)

Obviously, ideal profit distribution scheme cannot meetthe constraint conditions that all the interests of members ofthe distribution coefficient equal 1, so, there will be a discountfactor 𝑞𝑖, and the enterprise income distribution coefficient is

𝜑∗𝑖 = ℎ+𝑖 − 𝑞𝑖. (39)

For the strategic alliance members, the negative idealscheme is

ℎ− = {ℎ−1 , ℎ−2 , . . . , ℎ−𝑛 } . (40)

The actual allocation coefficient of the alliance member𝑖 should not be less than ℎ−𝑖 . Otherwise, the members of thealliance would have no initiative to join the alliance, and thesatisfaction rate is 𝜎𝑖 = ℎ−𝑖 /ℎ+𝑖 . Obviously, the greater theprofit distribution factor 𝜑∗𝑖 of each alliance member is, the

higher themember’s satisfaction degree (𝜎𝑖 = 𝜑∗𝑖 /ℎ+𝑖 ) of profitdistribution plan would be.

Nash has proposed a negotiation model in view of themultiple individual negotiation problem. In this paper, theasymmetric Nash negotiation model is used to solve the finalbenefit allocation of dynamic logistics alliance, that is, for theideal scheme ℎ+ = {ℎ+1 , ℎ+2 , . . . , ℎ+𝑛 }, seeking the best discountfactor 𝑞 = (𝑞1, 𝑞2, . . . , 𝑞𝑚). The negative value scheme of thestrategic alliance enterprise is taken as the starting point ofnegotiation; that is, ℎ− = (ℎ−1 , ℎ−2 , . . . , ℎ−𝑛 ). The asymmetricNash negotiation model is

max 𝑍 = 𝑚∏𝑖=1

(𝜑∗1ℎ+𝑖 −ℎ−1ℎ+𝑖 )𝜌𝑖 , (41)

s.t.𝑚∑𝑖−1

(ℎ+𝑖 − 𝑞𝑖) = 1,ℎ−𝑖 ≤ ℎ+𝑖 − 𝑞𝑖.

(42)

Formula (41) is the objective function of the Nashnegotiation model, where 𝜌𝑖 is the importance of the alliancemember 𝑖 in the entire alliance, which is determined bythe profit of the logistics enterprise. The (𝜑∗𝑖 − ℎ−𝑖 )/ℎ+𝑖 inthe objective function represents the gap between the finalprofit allocation factor of the member enterprise 𝑖 and thecoefficient of negative ideal allocation scheme. The greaterthe gap between the two is, the higher the satisfaction degreeof strategic alliance enterprises 𝑖 would be. Therefore, themeaning of the objective function is that all the strategicalliance enterprises could achieve a relatively satisfactoryresult through negotiation.

Formula (42) is the constraint condition of Nash nego-tiation model, in which ∑𝑚𝑖−1(ℎ+𝑖 − 𝑞𝑖) = 1 represents thefact that the sum of the final profit sharing coefficients of allstrategic alliance members is 1; ℎ−𝑖 ≤ ℎ+𝑖 −𝑞𝑖 indicates that theultimate profit distribution factor of alliance enterprise 𝑖 is notless than ℎ−𝑖 . Otherwise, it would mean that the negotiationsfail and the alliance member 𝑖 would withdraw from thenegotiation.

The solution can be obtained by the Kuhn-Tucker condi-tion:

𝑞∗𝑖 = ℎ+𝑖 − ℎ−𝑖 − 𝜌𝑖(1 − 𝑚∑𝑖=1

ℎ−𝑖 ) 𝜌𝑖ℎ+𝑖∑𝑚𝑖=1 𝜌𝑖ℎ+𝑖 . (43)

The profit distribution coefficient of member 𝑖 is𝜙∗ = ℎ−𝑖 + 𝜌𝑖(1 − 𝑚∑

𝑖=1

ℎ−𝑖 ) 𝜌𝑖ℎ+𝑖∑𝑚𝑖=1 𝜌𝑖ℎ+𝑖 . (44)

In above, it is obtained that the final profit distributioncoefficient of the alliance members is 𝜙∗𝑖 . According to 𝜙∗𝑖 ,we can get the satisfaction degree of the strategic alliance

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Table 2: Basic situation and profit of major express enterprises in China.

Company Cainiao SF EMSZTO YTO STO Yunda

Number of terminal nodes 23000 24000 10000 24000 12068 45000Number of franchisees 7700 2610 1495 2800 - -Number of transport centers 74 82 82 57 294 -Number of direct transport centers 68 60 48 54 294 -Direct ratio 91.9% 73.2% 58.5% 94.7% 100% 100%Net profit (2016) 21.65 14 12.4 12 41.8 10.3

60.05Note. Data before December 31, 2016.

enterprise 𝜎𝑖 and then normalize the satisfaction of eachalliance member:

𝜓𝑖 = 𝜎𝑖∑𝑚𝑖=1 𝜎𝑖 ,𝑚∑𝑖=1

𝜓𝑖 = 1;(45)

Δ𝜓𝑖 = 𝜓𝑖 − 1𝑛 , 𝑖 = 1, 2, . . . , 𝑛,𝑛∑𝑖=1

Δ𝜓𝑖 = 0. (46)

If Δ𝜓𝑖 ≥ 0, this proves that the member’s satisfactiondegree of the profit distribution scheme is higher than theaverage level of the alliancemembers, and then the profit levelof the member about satisfaction level should be reduced. IfΔ𝜓𝑖 ≤ 0, this proves that the member’s satisfaction degree ofthe profit distribution scheme is lower than the average levelof the alliance members, and the profit level of the memberabout satisfaction level should be increased. Considering thecontribution and satisfaction of strategic alliance members,we could get the profit allocation that should be assigned tothe private parties.

5. Case Study

5.1. The Basic Situation of the Major Express Enterprises inChina. In recent years, the express industry has developedrapidly in China, as the basic situation including express vol-ume, express revenue, and growth rate is shown in Figure 4.

Large express enterprises in China mainly include YTO,STO, ZTO, Yunda, SF, and EMS. The YTO, STO, ZTO,Yunda, and Cainiao part of Alibaba established a cooperativerelationship, whichmeans YTO, STO, ZTO, and Yunda coulddeliver goods, pick up goods, and handle other businessthrough offline Cainiao post. In order to facilitate the cal-culation, this paper would merge “Three Tong One Da” asa Cainiao company and then select Cainiao, SF, and EMS asthe research object for case analysis. The basic situation andprofits of the major express enterprises in China are shown inTable 2.

700

600

500

400

300

200

100

05.69

105.5

9.19

144.2

13.96

204.5

20.67

277

31.28

397.4

70

60

50

40

30

20

10

0

(%)

Express volume (billion pieces)Express revenue (billion yuan)

Growth rateGrowth rate

Figure 4: Development of express industry in China.

5.2. Initial Profit Allocation. According to industry estimates,the net profit of main express companies in China wouldgrow further in 2017. To facilitate the calculation, this paperassumes that the net profit of China’smajor express enterprisewould increase by 20%–30%, by which the profit range of themain express company in 2017 could be obtained. At the sametime, if the express company establishes strategic alliances,the alliance could reduce the logistics process logistics costsand time costs through the integration of information, ware-housing, transportation, distribution, and other resources.For example, the truck loading rate in China’s logisticsindustry is only about 50%–60%, far less than the UnitedStates and other developed countries. However, the vehicleloading rate could be greatly improved to reduce logisticscosts and increase the profit through the establishment ofstrategic alliance. It is assumed that the alliance between twoarbitrary express companies could achieve an effect of a 20%rise in profits through the integration of resources. The basicdata are obtained by integrated utilization of qualitative andquantitativemethods and shown in Table 3.The total revenueof the strategic alliance cooperation constructed by Cainiao,SF, and EMS is 190.83∼206.74 billion yuan.

For the convenience of calculation, we set Cainiao, SF,and EMS as 1, 2, and 3, and distribute profit though originalShapley value method. The data are allocated in Table 3.The profit quota for Cainiao is 𝑥1 = [93.86, 101.14]; referto Table 4 for specific calculations; it is easy to get SF and

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12 Complexity

Table 3: Express enterprise and strategic alliance profit.

Express enterprise Profit statement of express enterprise (unit: 100 million yuan)Cainiao SF EMS

Cainiao [72.06, 78.07] [146.66, 158.89] [103.3, 109.75]SF [146.66, 158.89] [50.16, 54.34] [75.02, 81.84]EMS [103.3, 109.75] [75.02, 81.84] [12.36, 13.39]

Table 4: Calculation table of profit allocation of Cainiao (unit: 100 million yuan).

𝑆 {1} {1, 2} {1, 3} {1, 2, 3}V(𝑠) [72.06, 78.07] [146.66, 158.89] [103.3, 109.75] [190.83, 206.74]V(𝑠 \ {1}) [0, 0] [50.16, 54.34] [12.36, 13.39] [75.02, 81.84]V(𝑠) − V(𝑠 \ {1}) [72.06, 78.07] [96.5, 104.55] [90.94, 96.36] [115.81, 124.9]|𝑠| 1 2 2 3𝜎(|𝑠|) 1/3 1/6 1/6 1/3𝜎(|𝑠|){V(𝑠) − V(𝑠 \ {1})} [24.02, 26.02] [16.08, 17.43] [15.16, 16.06] [38.60, 41.63]

Table 5: Decision matrices and weights.

Initial decision matrix𝑋 Weighted canonical matrix 𝑅Resource input Risk sharing & role positioning Effort level Resource input Risk sharing & role positioning Effort level

Cainiao ⟨0.85, 0.1⟩ ⟨0.75, 0.15⟩ ⟨0.75, 0.1⟩ ⟨0.34, 0.505⟩ ⟨0.22, 0.533⟩ ⟨0.263, 0.505⟩SF ⟨0.65, 0.2⟩ ⟨0.85, 0.1⟩ ⟨0.85, 0.1⟩ ⟨0.26, 0.56⟩ ⟨0.255, 0.505⟩ ⟨0.298, 0.505⟩EMS ⟨0.7, 0.15⟩ ⟨0.8, 0.15⟩ ⟨0.85, 0.1⟩ ⟨0.28, 0.533⟩ ⟨0.240, 0.533⟩ ⟨0.298, 0.505⟩𝑤 ⟨0.4, 0.45⟩ ⟨0.3, 0.45⟩ ⟨0.35, 0.45⟩ ⟨0.400, 0.450⟩ ⟨0.300, 0.45⟩ ⟨0.350, 0.450⟩EMS’s profit quota, which are 𝑥2 = [68.78, 75.33] and 𝑥3 =[28.19, 30.27], respectively. The test shows that the aboveprofit distribution scheme meets the basic conditions forcooperation.

5.3. Profit Allocation Coefficient Correction

5.3.1. Profit Allocation Coefficient CorrectionBased on Contribution

(1) Decision Matrix and Weight of Evaluation Index. Theexperts put three evaluation indexes according to theresources input, risk bearing, role commitment, and the levelof effort to assess the three express enterprises.The evaluationinformation is then processed by statistics and expressed asintuitionistic fuzzy numbers, and the initial decision matrixis obtained. The initial decision matrix 𝑋 is transformedinto a weighted canonical matrix 𝑅 according to formula(26). Meanwhile, without the consideration of the influencefactors of corporation satisfaction, this section directly usesthe trapezoidal fuzzy number method to calculate the inputof resources, risk weights, roles, and effort level and obtainsthe membership and nonmembership of evaluation index bystatistical method, as shown in Table 5.

(2) Calculation of Ideal Solutions andTheir Related CorrectionCoefficients. Through the weighted norm matrix 𝑅 andformula (30)–(32), the positive ideal solution 𝐴+ and thenegative ideal solution 𝐴− of intuitionistic fuzzy sets couldbe obtained, as shown in Table 6.

Table 6: Positive and negative ideal solutions of intuitionistic fuzzysets.

Ideal solution 𝑏1 𝑏2 𝑏3𝐴+ ⟨0.34, 0.505⟩ ⟨0.255, 0.505⟩ ⟨0.298, 0.505⟩𝐴− ⟨0.26, 0.56⟩ ⟨0.22, 0.533⟩ ⟨0.263, 0.505⟩Table 7: Profit allocation correction coefficient of strategic allianceenterprises.

Enterprise 𝑑 (𝑎𝑖, 𝐴+) 𝑑 (𝑎𝑖, 𝐴−) 𝜆𝑖 𝜃𝑖 Δ𝜃𝑖Cainiao 0.0473 0.0708 0.599 0.409 0.075SF 0.0708 0.0475 0.404 0.276 −0.057EMS 0.0538 0.0468 0.465 0.315 −0.018

Formulas (33) are used to calculate the distance 𝑑(𝑎𝑖, 𝐴+),𝑑(𝑎𝑖, 𝐴−) from enterprise 𝑎𝑗 to positive and negative idealsolution, respectively.Thenwe calculate the relative closenessdegree 𝜆𝑖 from enterprise 𝑎𝑗 to intuitionistic fuzzy positiveideal solution 𝐴+ according to formula (34) (35), normalizethe relative closeness degree 𝜆𝑖 to obtain the 𝜃𝑖, and finally getthe correction factorΔ𝜃𝑖 by formula (35), as shown in Table 7.

5.3.2. Profit Allocation Coefficient Correction Based on Stake-holder Satisfaction. At the end of the logistics project, inorder to get a fairer andmore reasonable allocation of profits,it is important to consider the impact of stakeholder satisfac-tion on the profits allocation after considering resource input,risk taking, role positioning, and effort level.

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Complexity 13

Table 8: Correction factor of enterprise profit distribution.

Enterprise 𝜌 ℎ+ ℎ− 𝑞∗ 𝜑∗ 𝜎𝑖 𝜓𝑖 Δ𝜓𝑖Cainiao 6/11 6/11 1/3 0.1325 0.413 0.7572 0.413 0.08SF 4/11 5/11 1/3 0.0916 0.3629 0.7984 0.435 0.101EMS 1/11 1/3 1/11 0.2403 0.0930 0.279 0.152 −0.181

Table 9: Profit distribution comparison.

Enterprise Interval Shapley Only consideringcontribution

Only considering stakeholdersatisfaction Comprehensive consideration

Cainiao [93.86, 101.14] [99.55, 107.28] [90.79, 97.89] [96.39, 103.89]SF [68.78, 75.33] [64.84, 70.16] [64.99, 97.89] [60.51, 66.34]EMS [28.19, 30.27] [26.44, 29.3] [35.05, 37.61] [33.93, 36.51]

According to the Cainiao, SF, and EMS express enter-prises’ annual profits before alliances, we reasonably assumethat the profit allocation coefficients proposed by the novice,SF, and EMS express companies are

ℎ = [[[ℎ11 ℎ21 ℎ31ℎ12 ℎ22 ℎ32ℎ13 ℎ23 ℎ33

]]]𝑇

=[[[[[[[[

611 411 111511 511 11113 13 13

]]]]]]]]

𝑇

. (47)

According to formula (38)–(46), by asymmetric Nashcoordination, the profit allocation model is computed. Weobtain importance vector of strategic alliance members 𝜌,government’s ideal profit allocation scheme ℎ+, negativeideal profit allocation scheme ℎ−, profit discount factor 𝑞∗𝑖 ,partition coefficient 𝜑∗𝑖 , and satisfaction degree 𝜎𝑖, as shownin Table 8.

5.4. Result Analysis. In Section 5.3 we have obtained theprofit allocation based on interval Shapley value, the profitallocation correction factor considering contribution, andthe stakeholder satisfaction. Therefore, the strategic allianceenterprises’ profit allocation could be calculated and shownin Table 9. It is worth mentioning that the profit allocationresult is not the final result, and the alliance also needs to paypersonal wages. The total wages needed to be paid should bedetermined according to the actual amount and average pricein the actual world.

The results of the profit allocation meet the successfulconditions of cooperation. The following could be foundaccording to the comparison:(1) Compared to the initial allocation scheme of intervalShapley value, only considering the contribution of strategicalliance, the profits of the Cainiao would increase, while theprofits of SF and EMS would be reduced to some extent. It isfully reflected by the contribution of the Cainiao enterprisesin the formation of strategic alliances, especially in resourceinput, which is far more than the SF and EMS company.(2) Compared to the initial allocation scheme of intervalShapley value, only considering the stakeholder satisfaction

of strategic alliance, EMS company profits would grow bynearly 25%, while Cainiao and SF’s profits would be reducedto a certain extent.Huge gap of operating profitswould lead tothe difference between the initial profit allocation coefficientsof the three proposed enterprises. As a consequence, thesatisfaction degree of profit allocation coefficient of EMS islower than the average value, which needs compensation.(3)Thecomprehensive profit allocation scheme considersthe impact of enterprise contribution and the satisfactionof the initial distribution plan of alliance members, whichensures the profit distribution of the fair and protects theenthusiasm of the alliance members and the stability ofalliance. Overall, compared to the initial allocation scheme ofinterval Shapley value, in the scheme of comprehensive profitdistribution, the profits of Cainiao and EMS increase a little,while those of SF decreased slightly, which is the result ofconsidering the contribution and satisfaction of themembersin the alliance.

6. Conclusions

This paper makes full use of social idle logistics capabil-ity to provide service to the first mile and the last mileof logistics based on the assumption that all the societylogistics providers build logistics strategic alliances, analyzecustomer’s logistics preference through the customer shop-ping behavior data recorded by the electricity supplier, andthen classify them as higher speed, lower cost, and lowercarbon emissions, respectively.The paper constructs differentobjective function models according to different preferencesand puts forward a kind of intensive distribution logisticsnetwork design considering sharing economy,which is solvedby metaheuristics to get the approximate solution, to providea new direction for the study of cyclic network design.

The rationality of profit distribution is the key to thesustainable operation of the alliance. Considering the benefitsfuzziness of the actual cooperative game, an improved inter-val Shapley value method which considers both the membersatisfaction and the contribution is proposed to make upfor the deficiency of the similar research. Taking the majorexpress enterprises in China as a case study, the results showthat, compared with the traditional interval Shapley value

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14 Complexity

method, the profit distribution coefficient calculated from theimproved interval Shapley value model could not only makeprofit distribution more equitable, but also improve stake-holder satisfaction to promote the sustainable developmentof alliance.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work is supported by the Natural Science Foundationof China (NSFC no. 71403225 and no. 71728001), the EnergyFoundation (no. G-1709-26901), the Soft Science Foundationof Sichuan Province and Chengdu City (no. 2014ZR0019/no.2015-RK00-00220-ZF), and the Cyclic Economic Center ofSichuan Province (Project no. XHJJ-1411).

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