Approach to solve multi‐criteria group decision‐making problems by exponential operational law in generalised spherical fuzzy environment. Issue 2 (14th April 2020)

• Record Type: Journal Article
• Authors: Haque, Tipu Sultan; Chakraborty, Avishek; Mondal, Sankar Prasad; Alam, Shariful
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 2(2020)
• Page Start: 106
• Page End: 114
• Publication Date: 2020-04-14
• Subjects: fuzzy set theory -- decision making -- operations research -- sensitivity analysis -- algebra; generalised spherical fuzzy set -- exponential operational law -- generalised spherical weighted exponential averaging operator -- multicriteria group decision‐making method -- algebraic properties -- sensitivity analysis; C1110 Algebra -- C1140E Game theory -- C1160 Combinatorial mathematics -- C1290 Applications of systems theory; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0078
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : The construction of generalised spherical fuzzy number (GSFN) is a reliable philosophy to design and understanding of vagueness and impreciseness. In this study, at first, the authors have defined the generalised spherical fuzzy set and discussed its several properties. Then, a new score and accuracy functions have been introduced in the generalised spherical fuzzy environment which leads to a new method of conversion of fuzzy number into a crisp number. New exponential operational law has been defined for GSFNs where the bases are positive real numbers & components are GSFNs and its various algebraic properties have been studied explicitly. Using this exponential operational law, a generalised spherical weighted exponential averaging operator has been proposed, which is used to develop a multi‐criteria group decision‐making (MCGDM) method in the generalised spherical fuzzy environment. The newly developed MCGDM has been demonstrated through a real‐life problem and its effectiveness and rationality have been shown through sensitivity analysis.