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A Novel Pythagorean Group Decision-Making Method Based on Evidence Theory and Interactive Power Averaging Operator
Author(s) -
Zhaohe Wan,
Minghua Shi,
Feng Yang,
Zhu Guang
Publication year - 2021
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1155/2021/9964422
Subject(s) - pythagorean theorem , group decision making , operator (biology) , computer science , fuzzy logic , kansei , artificial intelligence , data mining , machine learning , mathematics , biochemistry , chemistry , geometry , repressor , political science , transcription factor , law , gene
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.

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