Sustainable And Resilient Supplier Selection Using Improved Topsis And Intuitionistic Fuzzy Best - Worst Method

Abstract

Addressing supplier selection (SSPs) for the food industry is one of the main strategic issues throughout the whole supply chain to maximize corporate competitive advantage. As a result of regulatory obligations and market developments, organizations must enhance supplier selection procedures by researching and selecting suppliers based on sustainability factors (economic, environmental, and social). The study of sustainable resilient supplier selection problems (SRSSPs) is considered as a multi-criteria group decision problem (MCGDM), in which group of the alternatives is examined according to the multi-criteria. With the recent COVID-19 pandemic's serious effects on the supply chain, the concept of resilience and its function in SSPs is becoming increasingly important. Traditional MCGDM approaches such as AHP (Analytical Hierarchy Process) or ANP (Analytic Network Process) and TOPSIS (Ideal Solution Similarity Order Priority Technique) are commonly utilized to handle (SSPs). However, to produce consistent results, the conventional AHP or ANP must do a high number of pairwise comparisons, resulting in a computationally complex procedure. Meanwhile, the basic TOPSIS results are not conservative enough because only individual negative ideal solution is evaluated in supplier selection. This research provides a MCDM for solving MCGDM situations that incorporates intuitionistic fuzzy information. In the context of formalizing and addressing SSPs, the improved TOPSIS integration with Best Worst Methods (BWM) is deemed appropriate. To calculate the weights of the criterion, the best-worst fuzzy technique is examined first. The comprehensive TOPSIS method weighs the decision maker in the fuzzy environment using the provided proximity degrees. IFNs are designed to account for the ambiguity and uncertainty in the weightings of criteria and alternatives that are inherent in decision makers' subjective assessments. Furthermore, a technique for prioritizing alternatives based on the developed TOPSIS-based coefficient. A numerical example is used to demonstrate the model's applicability and efficacy. Four main criteria (economic, environmental, social, and resilience) and 16 sub-criteria of evaluation are recognized and classified. Finally, various sensitivity analysis methods, namely modifying the controlling parameter �, are used to test the robustness of the proposed framework and compare the study to other common fuzzy MCDM methods.