Resilient Supplier Selection in a Supply Chain by a New Interval-Valued Fuzzy Group Decision Model Based on Possibilistic Statistical Concepts

Supplier selection is one the main concern in the context of supply chain networks by considering their global and competitive features. Resilient supplier selection as generally new idea has not been addressed properly in the literature under uncertain conditions. Therefore, in this paper, a new multi-criteria group decision-making (MCGDM) model is introduced with interval-valued fuzzy sets (IVFSs) and fuzzy possibilistic statistical concepts. Then, a new weighting method for the supply chain experts or decision makers (DMs) is presented under uncertainty in supply chain networks. Additionally, a modified version of an entropy method is extended for computing the weight of each assessment criterion. Possibilistic mean, standard deviation, and the cube-root of skewness are proposed within the MCGDM. In addition, a new fuzzy ranking method based on relative-closeness coefficients are proposed to rank the resilient supplier candidates. Finally, a resilient supplier selection problem is solved by the proposed group decision model to demonstrate its validity and is compared with a recent study.