With the rapid development of China’s economy and the internet’s rapid popularization, online shopping has become one of the most important online behaviors. On 11 November 2015, on Alibaba’s annual day of discounts called the “Double 11 Shopping Carnival”, Taobao and Tmall (two e-commerce enterprises in China) generated more than $14.14 billion in sales and 678 million orders in just 24 h [1
]. However, the rapid development of online shopping has brought serious challenges to logistics. For example, for online shopping logistics service enterprises, the problems of high logistics costs, low operation efficiency and low resources utilization are serious [2
], and for end-customers, the timeliness and personalized needs of logistics distribution cannot be fully satisfied [2
In 2013, to cope with the online shopping logistics problem, the National Urban Distribution Development Guidelines
issued by the Ministry of Commerce of China promoted urban synergetic distribution to deal with the challenges posed by online shopping [7
]. In 2014, the Medium and Long Term Development Plan for Logistics from 2014 to 2020
issued by the China’s State Council set a development goal of cooperative operations among logistics enterprises to satisfy buyers’ personalized and diversified demand and reduce traffic pressure [8
]. Experts explored solutions for the online shopping logistics problem and proposed a novel logistics service mode, namely joint distribution (JD) [9
]. Currently, JD has drawn more and more attention from government, e-commerce enterprises and logistics enterprises. Development of JD is an effective way to reduce logistics costs, increase the utilization of logistics resources, improve the efficiency of logistics operations and satisfy the personalized demand of the last mile delivery for end customers [12
]. In addition, sharing logistics resources such as distribution vehicles and warehouse centers can minimize traffic congestion and environmental impacts [17
]. Any logistics enterprise stands to lose if trying to “go it alone” in the e-commerce environment [12
Therefore, logistics enterprises must collaborate with each other to realize the integration of resources, information sharing and business collaboration [12
]. General logistics service providers strongly believe in the potential benefits of cooperation to increase their profitability or to improve the quality of their services [19
]. This makes it imperative to assess alternatives to build a joint distribution alliance (JDA) among logistics enterprises and identify right technology for partner selection that can assist them to achieve operational efficiency for online shopping logistics distribution. However, existing information in this area of partner selection mainly concentrates on the evaluation of a single logistics enterprise without taking into account the partner combination. The alliance consisting of the best partners is not necessarily a high-performance one [20
]. Therefore, the performance of partner combinations criteria in the alliance is evaluated in this paper, not the single partner.
The methodologies for partner combination selection of JDA can be classified into four main categories: multi-criteria decision making approaches (MCDM), empirical studies, optimization approaches and hybrid approaches based on a combination of two or more of above. The MCDM methods [21
] include AHP [24
], Preference Ranking Organization Methods for Enrichment Evaluations (PROMETHEE) [25
], TOPSIS [26
], Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [27
], Elimination Et Choice Translating Reality (ELECTRE) [28
. Chen et al.
] establish a mechanism for partner selection that emphasizes the relation of criteria and motivation, taking the motivations of different enterprises’ needs for establishing strategic alliances. Liao et al.
] develop a hesitant fuzzy linguistic VIKOR method (HFL-VIKOR), motivated by the traditional VIKOR method. This method uses a hesitant fuzzy linguistic term set to represent decision makers’ qualitative assessments in the processes of decision making. Feng et al.
] propose a fuzzy multiple attribute decision-making (FMADM) method, which integrated the assessment data of individual and collaborative utilities, to achieve the final ranking of all candidate partners for partner selection of co-development alliances. Note that the collaborative attributes include resource complementarity, motivation and goal correspondence, overlapping knowledge bases and compatible cultures. Govindan et al.
] focus on green supplier evaluation and selection that considered environmental factors based on the literature review, while the limitations of current literature and the future direction of green supplier selection were analyzed. In addition, Govindan et al.
] also point out that social and environmental factors should be considered in the future development of logistics.
Empirical studies employed a variety of modes to collect data such as questionnaires, case studies, expert interviews and so on. Pidduck [34
] investigates a number of software industry enterprises in a supply chain and found that the collaborative partner selection was complex. The criteria of partner selection involved hard constraints, resource availability, social network, reputation, politics, ambiguity, personal friendship and prior relationships. Cui et al.
] establishes a determining-factor model for partner selection taking four hypothesized determining factors (mutual compatibility and degree of mutual trustworthiness, partners’ technical innovation, intellectual property right and availability of partners’ human capital) into consideration, based on the analysis of theoretical literature and data from an empirical survey questionnaire. Brian and Lisa [36
] explore small innovative firms’ motives for selecting university partners. Four types of motives including risk-reducing, cost-reducing, value creating and enabling motives were listed. The firms paid more attention to the enabling motives retaining flexibility and adaptability in commercializing the early-stage innovations.
Optimization methods involve minimizing or maximizing the objective function with a set of constraints. Amid et al.
] develop a fuzzy multi-objective linear model to overcome the vagueness and imprecision of information to select the right suppliers. The fuzzy sets theory was used to handle uncertainty and the objectives were to minimize the net cost, minimize the rejected items and minimize the net late deliveries. Yeh and Chuang [38
] introduce green criteria into the framework of partner criteria and developed an optimum mathematical planning model for green partner selection. The model involved four objectives such as cost, time, product quality and green appraisal score. Two multi-objective genetic algorithms were adopted to find the set of Pareto-optimal solutions. Niu et al.
] evaluate the candidate partners by investigating five attributes including cost, time, quality, reputation and risk in virtual enterprises and develop an enhanced ant colony optimizer (ACO) to address the partner selection problem. Dao et al.
] propose an innovative decision support system for partner selection in virtual enterprises using genetic algorithm (GA) with a unique dynamic chromosome representation and genetic operation. Wan et al.
] propose a new intuitionistic fuzzy linear programming model for the selection of logistics outsourcing providers. For the fuzzy information, the intuitionistic fuzzy linear programming model was solved by developed three kinds of approaches including the optimistic, pessimistic and mixed approaches depended on the non-membership functions.
The hybrid methods employ a combination of the methods stated above. Awasthi et al.
] present a fuzzy benefit-cost-opportunity-risk and gray relational analysis (BOCR-GRA) approach to collaboration partner selection of urban logistics planning with uncertainty. The evaluation criteria was identified using a BOCR framework, namely benefits, costs, opportunities and risks. Then GRA and five BOCR scoring methods were employed to select the optimal collaboration partner. Erkayman et al.
] evaluate the third-party logistics (3PL) provider by using the integrated fuzzy AHP and TOPSIS methods which considered price, general reputation, customer services, on-time delivery, information technologies and flexibility as evaluation criteria. Su and Chen [43
] develop a multilevel grey evaluation model combining grey evaluation and AHP. Four classification criteria of cost, service quality, cooperation reliability and comprehensive strength were designed from the long term cooperation perspective. Büyüközkana et al.
] propose a three-phase strategic alliance partner selection method in electronic logistics value chain. The first phase identified the strategic main and criteria of the alliance partner selection including strategic and business excellence. The second phase calculated the criteria weights using fuzzy AHP method. The third and final phase was to conduct fuzzy TOPSIS to obtain the optimal partner. Li et al.
] propose a customer satisfaction evaluation method of customized product development based on Voice-of-Customer (VoC) through integrating entropy weight and AHP method.
In general, fuzzy set theory is used to cope with the lack of quantitative data and uncertainty on the decision maker’s preference. The linguistic ratings of decision makers or experts are transformed as triangular fuzzy numbers. In this paper, we address the problem of JD among online shopping logistics enterprises to build a high-performance JDA. Our goal is to select the optimal partner combination by evaluating the partner combination performance for criteria with uncertainty. The main contributions of this paper are as follows:
(1) This is the first study that provides a complete and detailed list of evaluation criteria from the four aspects of economy, society, environment and flexibility for partner combination selection regarding online shopping from a sustainability perspective.
(2) In view of the problem that the index weight of TOPSIS evaluation method is difficult to determine, this paper uses the integrated fuzzy EW-AHP to determine the index weight, taking objective and subjective factors of experts into consideration. Then the fuzzy TOPSIS-based MCDM method is developed for a partner combination selection of JDA, which extends the application of the fuzzy TOPSIS method.
(3) In order to obtain better insight into the partner combination selection, a sensitivity analysis is performed to assess the impact of the index weight on final results, improving the outcome on uncertain partner combination selections.
The rest of this paper is organized as follows: Section 2
provides an economy-society-environment-flexibility (ESEF) framework to identify the evaluation criteria. In Section 3
, we propose an integrated fuzzy EW-AHP and TOPSIS method for partner combination selection. A numerical application is presented in Section 4
. Section 5
provides results and sensitivity analysis to evaluate the rationality and robustness of the results. The conclusion is provided in Section 6
The partner combinations of JDA are ranked by using integrated fuzzy EW-AHP and TOPSIS method. The result shows that the partner combination is the optimal alternative. In order to test the robustness of decision, a sensitivity analysis is performed in terms of the influence of criteria weight changes on the result.
shows those cases where the economic criteria have 5%, 10% and 20% less weight and 5%, 10% and 20% more weight than the base weight (i.e.,
the weight used in Section 4
). It can be seen that, as the economic criteria become more important, the relative closeness of the partner combinations
decreases slightly. The relative closeness of partner combinations
increases slightly, but the relative closeness of partner combination
is far removed from that of partner combination
. In this case,
always has the highest relative closeness. Therefore, no matter how the criteria weights of the economic criteria change,
is always the optimal partner combination, which indicates the robustness and effectiveness of the proposed methodology.
Cases where the societal criteria have 5%, 10% and 20% less weight and 5%, 10% and 20% more weight than the base weight are shown in Figure 4
. It can be seen that, as the societal criteria become more important, the relative closeness of partner combinations
changes with a slight increase. The relative closeness of partner combination
gets so far away from that of partner combination
. However, no matter how the criteria weights of the societal criteria change,
is always the optimal partner combination, which indicates the robustness and effectiveness of the proposed methodology.
shows those cases where the environmental criteria have 5%, 10% and 20% less weight and 5%, 10% and 20% more weight than the base weigh. It can be seen that the relative closeness of partner combinations
has the same variation trend with a slight decrease in the case of environment criteria fluctuation. Although the relative closeness of partner combinations
have the same variation trend with slight increase, the relative closeness of partner combination
increases faster than that of partner combination
is also the optimal partner combination no matter how the criteria weights of the environmental criteria change, which indicates the robustness and effectiveness of the proposed methodology.
Cases where the flexibility criteria have 5%, 10% and 20% less weight and 5%, 10% and 20% more weight than the base weigh are shown in Figure 6
. It can be seen that, as the flexibility criteria become more important, the relative closeness of partner combination
decreases, and it ranks fourth, surpassed by
. The relative closeness of partner combination
increases, which gets closer to that of alternative
(the best alternative). However, the partner combination
still ranks first. Therefore,
is always the optimal partner combination no matter how the criteria weights of the flexibility criteria change, which indicates the robustness and effectiveness of the proposed methodology.
From the above analysis, we can see that the partner combination always is the optimal alternative no matter how the criteria weights change using the integrated fuzzy EW-AHP and TOPSIS methodology. It indicates that the partner combination selection result, by employing integrated fuzzy EW-AHP and TOPSIS approach, is robust and effective.
In this paper, an integrated fuzzy EW-AHP and TOPSIS approach is proposed for evaluating partner combinations for JDA for online shopping. First, the partner combination evaluation index system is obtained using the ESEF framework based on the academic literature and experts that cooperated with our project team. The index system not only considers the economic factors, but also considers the societal, environmental and flexibility factors for sustainability.
Then, an integrated fuzzy EW-AHP approach is employed to determine the criteria weights taking objective and subjective factors of experts into consideration. At the same time, a fuzzy TOPSIS approach is used to select the optimal partner combination for JDA. In order to further verify the validity of the methodology, this paper selects the index data of partner combinations of JDA in Chongqing City, and applies the methodology to the empirical research. The results show that the proposed methodology is robust and effective, which has certain significance and reference for establishing a stable and sustainable JDA for online shopping.
The limitation of the proposed methodology is that the decision makers need to be proficient in the use of the criteria weights. If they lack the professional experience, this will impact on the results obtained. Another limitation is the lack of quantitative calculation methodology for criteria combination performance.
Taking the fast development of our society into consideration, in future studies, and the evaluation criteria system will be updated to move with the times. It is worth mentioning that the weights of criteria may need to be re-determined with the changes of the external environment. In the future, it is also necessary to develop a computer-based application system that supports decision making as it can speed up the implementation of proposed approaches and facilitate a man–machine interaction presentation of result analysis.