A possibility distribution‐based multicriteria decision algorithm for resilient supplier selection problems.

Thus far, limited research has been performed on resilient supplier selection—a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails key sourcing decision for an organization to gain competitive advantage. In the presence of multiple conflicting evaluation criteria, contradicting decision makers, and imprecise decision‐relevant information (DRI), this problem becomes even more difficult to solve with the classical optimization approaches. Possibility distribution‐based multicriteria decision analysis (MCDA) is a viable alternative approach for handling inherent uncertainty of imprecise DRI associated with the evaluation offered by a group of contradicting decision makers. However, prior research focusing on MCDA‐based supplier selection problem has been lacking in the ability to provide a seamless integration of numerical and linguistic evaluation criteria along with the consideration of multiple decision makers. To address these challenges, we present a comprehensive decision‐making framework for ranking a set of suppliers from resiliency perspective. The proposed algorithm is capable of leveraging imprecise and aggregated DRI obtained from crisp numerical assessments and reliability adjusted linguistic appraisals from a group of decision makers. We adapt two popular tools—single‐valued neutrosophic sets (SVNS) and interval‐valued fuzzy sets (IVFS)—and for the first time, extend them to incorporate both crisp and linguistic evaluations in a group decision‐making platform to obtain aggregated SVNS and IVFS decision matrixes. This information is then used to rank the resilient suppliers by using technique for order preference by similarity to an ideal solution method. We present a case study to illustrate the mechanism of the proposed algorithm. A sensitivity analysis demonstrates the strength of the proposed algorithm to generate alternative ranking scheme with respect to the shifting priorities of evaluation criteria and shows the reliability of the decision‐making framework. [ABSTRACT FROM AUTHOR]