Proportional Distribution Based Pythagorean Fuzzy Fairly Aggregation Operators With Multi-Criteria Decision-Making

Abstract

Pythagorean fuzzy sets (PyFSs) are an essential tool for characterizing fuzzy data in decision-making processes. In contrast to normal fuzzy structures, PyFSs feature a sum of squares of membership grades that is near a unit interval, which increases uncertainty. Within a Pythagorean fuzzy environment, we intend to build unique operational rules and aggregation operators (AOs) in this proposed work. The proposed work presents; notions, operational rules, and proportionate notions to establish a fair remedy for the membership degree (MSD) and non-membership degree (NMSD) characteristics of “Pythagorean fuzzy numbers” (PyFNs) along with algorithms. Our proposed AOs give more generalized, definitive, and precise information than earlier methods. If decision-makers (DMs) have partial weight information under PyFSs, then by combining with AOs, one can solve a “multi-criteria decision-making” (MCDM) problem by applying the proposed algorithms. To demonstrate the applicability and superiority of our unique technique, we present an example illustrating the efficacy of the suggested algorithm in resolving decision-making issues, and a comparison has been presented with existing approaches.

Keywords

• Linear programming
• aggregation operators
• fairly operations
• decision-making
• optimization model