Abstract
Since Pythagorean fuzzy sets can better reflect the cognition of the decision objects for experts, researchers have begun to pay increasingly more attention to them in recent years. The majority of the research on Pythagorean fuzzy environment assumes that the decision maker is completely rational and does not consider the correlation among the attribute variables. In view of the above, this paper proposes a method to solve the multiple attribute group decision-making problem based on D-S theory and interactive power averaging operator. First, the new Pythagorean fuzzy interactive weighted power average operator is designed to aggregate the attribute evaluation information given by experts one by one, and the comprehensive evaluation information of each expert is obtained. Then, the expert comprehensive evaluation information is aggregated by the rule of evidence combination to obtain the comprehensive evidence information and confidence interval of each candidate. Then, the decision-making method for candidate alternatives is performed by the possibility discriminant rule. The design method considers not only the decision makers’ bounded rationality but also the correlation among the attribute variables. Finally, the selection of the energy exploitation plan illustrates the feasibility and effectiveness of the proposed group decision method.
Highlights
Since Bellman and Zadeh formally proposed the concept of fuzzy decision in 1970, it has attracted a large number of scholars to study this field of decision-making due to its wide application [1]
(4) e decision method given in [27, 29] uses the PFWA operator and TOPSIS when aggregating the evaluation information of different attributes and the evaluation information between experts, respectively. is paper uses the PFIWPA operator and the evidence theory to deal with the characteristics of information aggregation at different stages. e proposed method integrates the advantages of multiple methods to comprehensively solve the problem of aggregating of group decision information
Aiming at the problem of multiple attribute group decisionmaking in Pythagorean fuzzy environment, this paper uses evidence theory and the Pythagorean fuzzy interactive weighted power average operator to propose a group decision-making method. e new method uses the Pythagorean fuzzy interactive weighted power average operator to aggregate experts’ attribute evaluation information and gives the comprehensive evaluation information of each expert. e operator used is better than the commonly used power average operator and satisfies idempotence and exchangeability and mines attribute associations. en, the rules of evidence synthesis are used to gather the comprehensive evaluation information of experts and give the comprehensive evidence information of the candidate program
Summary
Since Bellman and Zadeh formally proposed the concept of fuzzy decision in 1970, it has attracted a large number of scholars to study this field of decision-making due to its wide application [1]. In view of the fact that intuitionistic fuzzy sets can flexibly represent decision-making information from multiple perspectives of membership, nonmembership, and hesitation, it has attracted the attention of many scholars and has been widely used to solve fuzzy decision problems. The power average operator [22] is a classic fuzzy information gathering tool Since it was proposed, it has received attention from many scholars and has been extended to different fuzzy environments. E common power average operators are the hesitant fuzzy power aggregation operator, intuitionistic fuzzy power average operator, and triangular intuitionistic fuzzy power average operator; they are used to solve different ambiguities in the decision-making information aggregation problem in a fuzzy environment These power average operators can gather fuzzy information only from the perspective of the overall equilibrium and cannot capture the relevant information between attributes.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
