Enhancing sustainability in supply chain management using softmax Schweizer-Sklar information aggregation

Abstract

Selecting sustainable suppliers is crucial to achieving sustainability objectives within the supply chain. However, the inherent complexity and uncertainty associated with evaluating supplier performance necessitate the use of fuzzy decision-making tools. This paper proposes the use of cubic intuitionistic fuzzy sets (CIFS) with four aggregation operators (AOs) based on Schweizer-Sklar (SS) t-norm and t-conorm: cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted average (CIFSSSWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted average (CIFSSSOWA), cubic intuitionistic fuzzy softmax Schweizer-Sklar weighted geometric (CIFSSSWG), and cubic intuitionistic fuzzy softmax Schweizer-Sklar ordered weighted geometric (CIFSSSOWG). The inclusion of pseudocodes increases practical application. An exhaustive sensitivity analysis assesses the influence of different parameters on the effectiveness of these techniques. The advantages of precision, efficiency, and execution time are underscored in the comparative analysis, which offers valuable insights that can inform more informed decision-making regarding the selection of green suppliers. The results demonstrate the efficacy of all the proposed approaches in providing a comprehensive and accurate evaluation of green suppliers. This study helps green supplier selection practices get better and gives practical advice to supply chain professionals who want to make their operations more sustainable.