A Study and Application Analysis Exploring Pythagorean Fuzzy Set Distance Metrics in Decision Making

Author:

Thakur Palvinder1,Paradowski Bartosz2ORCID,Gandotra Neeraj1ORCID,Thakur Parul1ORCID,Saini Namita1ORCID,Sałabun Wojciech23ORCID

Affiliation:

1. Yogananda School of AI, Computer and Data Sciences, Shoolini University, Solan 173229, India

2. National Institute of Telecommunications, ul. Szachowa 1, 04-894 Warsaw, Poland

3. Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland

Abstract

The ever-increasing demand for high-quality solutions drives research toward more sophisticated decision-making solutions. In the field of decision making, the ability to solve complex real-world problems is of paramount importance. To this end, fuzzy sets are used, which offer the possibility of incorporating uncertainty into the values describing decision options. This study focuses on Pythagorean fuzzy sets, an extension of classical fuzzy sets, providing even more tools for modeling real-world problems by presenting a distance measure for these specific sets. A verification of the characteristics of the proposed distance measure has been carried out, proving its validity. The proposed measure is characterized by a more straightforward formula and thus simplifies the calculations. Furthermore, to confirm its usability, a multi-criteria decision-making methodology is presented, the results of which are compared with two multi-criteria decision-making methods, namely, PF-TOPSIS and PF-VIKOR, and another distance measure previously presented in the literature. The comparative analysis highlights lower variability in terms of preference values calculated using the proposed distance measure, which confirms the stability and reliability of the newly proposed distance measure while maintaining low computational complexity. Moreover, a high correlation with rankings calculated using PF-TOPSIS ensures its utility in terms of decision making.

Funder

National Science Center

Publisher

MDPI AG

Reference63 articles.

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