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Multiple attribute group decision making based on q‐rung orthopair fuzzy Heronian mean operators
Author(s) -
Liu Zhengmin,
Wang Song,
Liu Peide
Publication year - 2018
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.22032
Subject(s) - fuzzy logic , partition (number theory) , fuzzy set , generalization , mathematics , group decision making , computer science , operator (biology) , group (periodic table) , mathematical optimization , artificial intelligence , combinatorics , mathematical analysis , biochemistry , chemistry , organic chemistry , repressor , political science , transcription factor , law , gene
The q‐rung orthopair set (q‐ROFSs) can serve as a generalization of the existing orthopair fuzzy sets, including intuitionistic fuzzy sets and Pythagorean fuzzy sets. The most desirable characteristic of q‐ROFSs is that they support a greater space of allowable membership grades and provide decision makers more freedom in describing their true opinions. As a classical aggregation operator, Heronian mean (HM) can model the interrelationship between attributes. In this paper, we extend the traditional HM to aggregate q‐rung orthopair fuzzy information and propose the q‐rung orthopair fuzzy HM and its weighted form. Further, to overcome the shortcomings of the traditional HM, considering the possible partition structure in the actual decision situations, we propose the q‐rung orthopair fuzzy partitioned Heronian mean operator and the q‐rung orthopair fuzzy weighted partitioned Heronian mean operator. Then, some special cases and some desirable properties are investigated and discussed. A new multiple attribute group decision‐making(MAGDM) technique is developed based on the proposed q‐rung orthopair fuzzy operators. Finally, a representative example is provided to verify the effectiveness and superiority of the proposed method by comparing with other several existing representative MAGDM methods.