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SVNMPR: A new single‐valued neutrosophic multiplicative preference relation and their application to decision‐making process.

Source :
International Journal of Intelligent Systems; Mar2022, Vol. 37 Issue 3, p2089-2130, 42p
Publication Year :
2022

Abstract

The aim of the paper is to present the concept of a multiplicative preference relation (MPR) with the features of the single‐valued neutrosophic (SVN) set and named as SVN multiplicative preference relation (SVNMPR). The SVN set (SVNS) handles uncertainties more broadly than the intuitionistic fuzzy set by considering the three independent degree systems with a scale rating of 0–1 and is symmetrical about 0.5. However, for asymmetrical distribution, Saaty discusses the 1–9 scale to display the information. Driven by the advantages of the scale of 1–9 and the independence nature of degrees in SVNS, in this paper, we introduce the concept of SVNMPR with ratings values as SVN numbers with range 1∕q−q with q>1. The main advantages of the proposed relation are the asymmetric and nonuniform distributions of about 1. To complete address this, we divide the stated work into three parts. First, an idea of SVNMPR is added wherein pairwise evaluation values are represented the usage of SVN numbers over a scale of 1∕q−q with q>1. Also, we outline the idea of the SVN multiplicative preference set (SVNMPS). Second, to rank the proposed SVNMPS, we define a new ranking method with improved score functions by adding the expert's attitude to the analysis. The relationship between the proposed and the existing functions is also derived. Third, we are defining some new operation laws for SVNMPS to preserve the closeness property. On the basis of these laws, we define a number of operators to aggregate the different pairs of the SVNMPSs and to derive a number of relations and inequalities. Finally, a group decision‐making approach is established and the practicality of it is demonstrated with a numerical example and compared with a number of existing approaches. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08848173
Volume :
37
Issue :
3
Database :
Complementary Index
Journal :
International Journal of Intelligent Systems
Publication Type :
Academic Journal
Accession number :
154863098
Full Text :
https://doi.org/10.1002/int.22767