Pythagorean fuzzy Schweizer and Sklar power aggregation operators for solving multi-attribute decision-making problems

Abstract

The objective of this paper is to develop Pythagorean fuzzy (PF) aggregation operators, utilizing the concept of power aggregation operators through Schweizer and Sklar (SS) operations. A series of aggregation operators, viz., PF SS power average operator, PF SS power weighted average operator, PF SS power geometric operator, and PF SS power weighted geometric operator under PF environment is proposed in this paper. The developed operators possess the capacity to make information aggregation technique more flexible than other existing operators due to the presence of SS t-norms and t-conorms in PF environment. Also, for the appearance of power aggregation operator, the developed operators contain the capability to eliminate effects of unreasonable data from biased decision makers by considering interrelationships among the fused arguments. Several properties of the proposed operators are studied and a method for solving multi-attribute decision-making problems under PF context is developed. To illustrate the proposed method and to show its efficiency, an example, studied previously, is solved and compared with existing methods.