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Pythagorean fuzzy multiattribute group decision making based on risk attitude and evidential reasoning methodology
Author(s) -
Peng Benhong,
Zheng Chaoyu,
Zhao Xuan,
Wei Guo,
Wan Anxia
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22547
Subject(s) - ranking (information retrieval) , decision matrix , group decision making , operator (biology) , pythagorean theorem , evidential reasoning approach , computer science , measure (data warehouse) , credibility , closeness , fuzzy logic , aggregate (composite) , value (mathematics) , matrix (chemical analysis) , data mining , mathematics , artificial intelligence , operations research , machine learning , decision support system , business decision mapping , chemistry , geometry , materials science , repressor , law , mathematical analysis , composite material , biochemistry , political science , transcription factor , gene
Two aspects of problems including selection of aggregation operator for extreme fuzzy evaluation value and risk attitude of decision makers cannot be well solved in Pythagorean fuzzy (PF) multiattribute group decision making (MAGDM). This paper extends the evidential reasoning aggregation method in the intuitionistic fuzzy environment, expands the dictionary ranking method by constructing interval‐valued numbers through the proposed credibility functions of PF values and the concept of closeness degree, widens the continuous generalized ordered weighted average ( C ‐ GOWA ) operator to establish a risk attitude ranking measure, and puts forward a PF MAGDM approach based on risk attitude and evidence reasoning methodology (ERM). First, the proposed method utilizes the ERM to aggregate each decision maker's decision matrix and the weights of the attributes to get his/her aggregated decision matrix. Then, it incorporates the obtained aggregated decision matrices of the experts, the weights of the experts and the ERM to accomplish the aggregated PF value of each alternative. Finally, the ranking measure value of risk attitude on each alternative's PF value is calculated, and the sensitivity analysis on the ranking measure function is carried out. The proposed method has overcome the drawbacks of the existing methods for fuzzy MAGDM in PF environments.