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Thus far, limited research has been performed on resilient supplier selection- a problem that requires simultaneous consideration of a set of numerical andlinguistic evaluation criteria, which are substantially different fromtraditional supplier selection problem. Essentially, resilient supplierselection entails key sourcing decision for an organization to gain competitiveadvantage. 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 classicaloptimization approaches. However, prior research focusing on MCDA basedsupplier selection problem has been lacking in the ability to provide aseamless integration of numerical and linguistic evaluation criteria along withthe consideration of multiple decision makers. To address these challenges, wepresent a comprehensive decision-making framework for ranking a set ofsuppliers from resiliency perspective. The proposed algorithm is capable ofleveraging imprecise and aggregated DRI obtained from crisp numericalassessments and reliability adjusted linguistic appraisals from a group ofdecision makers. We adapt two popular tools - Single Valued Neutrosophic Sets(SVNS) and Interval-valued fuzzy sets (IVFS), and for the first time extendthem to incorporate both crisp and linguistic evaluations in a group decisionmaking platform to obtain aggregated SVNS and IVFS decision matrix. Thisinformation is then used to rank the resilient suppliers by using TOPSISmethod. We present a case study to illustrate the mechanism of the proposedalgorithm.

A Possibility Distribution Based Multi-Criteria Decision Algorithm for Resilient Supplier Selection Problems