Gamified approach towards optimizing supplier selection through Pythagorean Fuzzy soft-max aggregation operators for healthcare applicationsopen access
- Authors
- Shahab, Sana; Anjum, Mohd; Dutta, Ashit Kumar; Ahmad, Shabir
- Issue Date
- Mar-2024
- Publisher
- AMER INST MATHEMATICAL SCIENCES-AIMS
- Keywords
- soft-max function; aggregation operators; decision-making; Pythagorean fuzzy number
- Citation
- AIMS MATHEMATICS, v.9, no.3, pp 6738 - 6771
- Pages
- 34
- Journal Title
- AIMS MATHEMATICS
- Volume
- 9
- Number
- 3
- Start Page
- 6738
- End Page
- 6771
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90832
- DOI
- 10.3934/math.2024329
- ISSN
- 2473-6988
2473-6988
- 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.
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