| dc.description.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. | en_US |
