Gamified approach towards optimizing supplier selection through Pythagorean Fuzzy soft-max aggregation operators for healthcare applications

Abstract

The soft -max function, a well-known extension of the logistic function, has been extensively utilized in numerous stochastic classification m ethodologies, s uch a s l inear differential analysis, soft -max extrapolation, naive Bayes detectors, and neural networks. The focus of this study is the development of soft -max based fuzzy aggregation operators (AOs) for Pythagorean fuzzy sets (PyFS), capitalizing on the benefits provided by the soft -max function. In addition to introducing these novel AOs, we also present a comprehensive approach to multi -attribute decision -making (MADM) that employs the proposed operators. To demonstrate the efficacy and applicability of our MADM method, we applied it to a real -world problem involving Pythagorean fuzzy data. The analysis of supplier selection has been extensively examined in many academic works as a crucial component of supply chain management (SCM), recognised as a significant MADM c hallenge. The process of choosing healthcare suppliers is a pivotal element that has the potential to greatly influence the efficacy and calibre of healthcare provisions. In addition, we given a numerical example to rigorously evaluate the accuracy and dependability of the proposed procedures. This examination demonstrates the effectiveness and potential of our proposed soft -max based AOs and their applicability in Pythagorean fuzzy environments.