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Confidence levels q ‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems
Author(s) -
Joshi Bhagawati Prasad,
Gegov Alexander
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.22203
Subject(s) - multiple criteria decision analysis , fuzzy logic , bounded function , fuzzy set , extension (predicate logic) , set (abstract data type) , computer science , power (physics) , mathematics , fuzzy number , mathematical optimization , artificial intelligence , mathematical analysis , physics , quantum mechanics , programming language
The concept of q ‐rung orthopair fuzzy set ( q ‐ROFS) is the extension of intuitionistic fuzzy set (IFS) in which the sum of the q th power of the support for and the q th power of the support against is bounded by one. Therefore, the q ‐ROFSs are an important way to express uncertain information in broader space, and they are superior to the IFSs and the Pythagorean fuzzy sets. In this paper, the familiarity degree of the experts with the evaluated objects is incorporated to the initial assessments under q ‐rung orthopair fuzzy environment. For this, some aggregation operators are proposed to combine these two types of information. Their some important properties are also well proved. Furthermore, these developed operators are utilized in a multicriteria decision‐making approach and demonstrated with a real life problem of customers' choice. Then, the experimental results are compared with other existing methods to show its superiority over recent research works.