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Multiple‐attribute group decision‐making based on power Bonferroni operators of linguistic q ‐rung orthopair fuzzy numbers
Author(s) -
Liu Peide,
Liu Weiqiao
Publication year - 2019
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.22071
Subject(s) - fuzzy logic , operator (biology) , mathematics , fuzzy set , group decision making , linguistics , fuzzy number , power (physics) , computer science , algorithm , artificial intelligence , psychology , social psychology , philosophy , physics , quantum mechanics , biochemistry , chemistry , repressor , transcription factor , gene
In this paper, a new conception of linguistic q ‐rung orthopair fuzzy number (Lq‐ROFN) is proposed where the membership and nonmembership of the q ‐rung orthopair fuzzy numbers ( q ‐ROFNs) are represented as linguistic variables. Compared with linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers, the Lq‐ROFNs can more fully describe the linguistic assessment information by considering the parameter q to adjust the range of fuzzy information. To deal with the multiple‐attribute group decision‐making (MAGDM) problems with Lq‐ROFNs, we proposed the linguistic score and accuracy functions of the Lq‐ROFNs. Further, we introduce and prove the operational rules and the related properties characters of Lq‐ROFNs. For aggregating the Lq‐ROFN assessment information, some aggregation operators are developed, involving the linguistic q ‐rung orthopair fuzzy power Bonferroni mean (BM) operator, linguistic q ‐rung orthopair fuzzy weighted power BM operator, linguistic q ‐rung orthopair fuzzy power geometric BM (GBM) operator, and linguistic q ‐rung orthopair fuzzy weighted power GBM operator, and then presents their rational properties and particular cases, which cannot only reduce the influences of some unreasonable data caused by the biased decision‐makers, but also can take the interrelationship between any two different attributes into account. Finally, we propose a method to handle the MAGDM under the environment of Lq‐ROFNs by using the new proposed operators. Further, several examples are given to show the validity and superiority of the proposed method by comparing with other existing MAGDM methods.