Total ordering for intuitionistic fuzzy numbers. (17th May 2016)

• Record Type: Journal Article
• Authors: Lakshmana Gomathi Nayagam, V.; Jeevaraj, S.; Geetha, Sivaraman
• Is Part Of: Complexity. Volume 21(2016)Supplement 2
• Page Start: 54
• Page End: 66
• Publication Date: 2016-05-17
• Subjects: intuitionistic fuzzy number -- upper lower dense sequence -- total ordering -- intuitionistic fuzzy interval number -- trapezoidal intuitionistic fuzzy number -- membership -- non‐membership -- vague and imprecise score functions -- lexicography; Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1002/cplx.21783
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Intuitionistic fuzzy set plays a vital role in decision making, data analysis, and artificial intelligence. Many decision‐making problems consist of different types of datum, where fuzzy set theoretical approaches may fail to obtain the optimal decision. Numerous approaches for intuitionistic fuzzy decision‐making problem have been introduced in the literature to overcome these short comings. But there is no single approach that can be used to solve all kinds of problems because of the partial ordering defined on the collection of intuitionistic fuzzy numbers (IFNs). Even though ranking of fuzzy numbers have been studied from early seventies in the last century, a total order on the entire class of fuzzy numbers has been introduced by Wang and Wang ( Fuzzy Sets Syst 2014, 243, 131–141) only on 2014. A total order on the collection of all IFN is an open problem till today. In this article, a total order on the entire class of IFN using upper lower dense sequence in the interval [0, 1] is proposed and compared with existing techniques using illustrative examples, further an algorithm (which is problem independent) for solving any intuitionistic fuzzy multicriteria decision‐making problem (Intuitionistic fuzzy MCDM) is introduced. This new total ordering on IFNs generalizes the total ordering defined in Wang and Wang (22 ) for fuzzy numbers. © 2016 Wiley Periodicals, Inc. Complexity 21: 54–66, 2016