Evidential Supplier Selection Based on DEMATEL and Game Theory

Tianyu Liu, Yong Deng, Tung Sun Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

182 Citations (Scopus)


The supplier selection plays an important role in supplier chain management. How to evaluate the performance of suppliers is still an open issue. Multi-criteria decision-making (MCDM), due to its ability of solving multi-source information problem, has become a quite effective tool. Currently, the analytic network process (ANP) and Entropy weight are employed to solved MCDM problems. However, these techniques ignore the one-sidedness of the single weighting method and cannot deal with the uncertainties of input data. In this paper, a new evidential ANP methodology based on game theory is proposed to efficiently address supplier management under uncertain environment. First, ANP and entropy weight are employed to obtain the subjective and objective weights of criteria. Second, based on decision-making trial and evaluation laboratory (DEMATEL) and game theory, the comprehensive weight of ANP and entropy weight can be determined. Game theory is employed to combine the merits of subjective weight and objective weight, and DEMATEL is adopted to adjust the weight of criteria to make the result more reasonable. Finally, evidence theory is adopted to deal with the uncertainties of input data and get the supplier selection result. A case study is given to demonstrate the proposed modeling process. By comparing with the existing methods, we demonstrate that the proposed model has many advantages and it shows the efficiency and rationality in supplier selection problem.
Original languageEnglish
Pages (from-to)1321-1333
Number of pages13
JournalInternational Journal of Fuzzy Systems
Issue number4
Publication statusPublished - 1 Apr 2018


  • ANP
  • Dempster–Shafer evidence theory
  • Entropy weight
  • Game theory
  • MCDM
  • Supplier selection

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence


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