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Consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q‐rung orthopair fuzzy set based on correlation measure
Author(s) -
Liu Donghai,
Huang An
Publication year - 2020
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.22215
Subject(s) - probabilistic logic , ranking (information retrieval) , topsis , measure (data warehouse) , fuzzy set , mathematics , fuzzy logic , fuzzy number , operator (biology) , score , rule based machine translation , computer science , data mining , artificial intelligence , statistics , operations research , biochemistry , chemistry , repressor , transcription factor , gene
The paper proposes a consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q‐rung orthopair fuzzy sets (PLq‐ROFSs) based on correlation measure. First, the operational laws of adjusted PLq‐ROFSs based on linguistic scale function (LSF) for semantics of linguistic terms are introduced, where the PLq‐ROFSs have same probability space. In addition, we define the score function and accuracy function of PLq‐ROFS based on the proposed operational laws to compare the PLq‐ROFSs. Furthermore, we propose the probabilistic linguistic q‐rung orthopair fuzzy weighted averaging (PLq‐ROFWA) operator and the probabilistic linguistic q‐rung orthopair fuzzy order weighted averaging (PLq‐ROFOWA) operator to aggregate the linguistic decision information. Considering the inconsistency between the individual information and aggregated information in decision‐making process and the demiddle of given linguistic sets tocision makers' behavioral factors, we define a new correlation measure based on LSF to develop a consensus reaching process for fuzzy behavioral TOPSIS method with PLq‐ROFSs. Finally, a numerical example concerning the selection of optimal green enterprise is given to illustrate the feasibility of the proposed method and some comparative analyses with the existing methods are given to show its effectiveness. The sensitivity analysis and stability analysis of the proposed method on the ranking results are also discussed.