Open AccessMultiple‐criteria decision analysis process by using prospect decision theory in interval‐valued neutrosophic environment
Author(s) -
Veerappan Chinnadurai,
Albert Bobin
Publication year - 2020
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2020.0040
Subject(s) - ranking (information retrieval) , prospect theory , multiple criteria decision analysis , dimension (graph theory) , interval (graph theory) , computer science , set (abstract data type) , indeterminacy (philosophy) , decision theory , mathematics , artificial intelligence , data mining , mathematical optimization , statistics , combinatorics , physics , finance , quantum mechanics , pure mathematics , economics , programming language
This study intends to present an innovative study for ranking the alternatives in multiple‐criteria decision analysis (MCDA) problems under the interval‐valued neutrosophic soft set (IVNSS) environment. In this study, to illustrate the notion of objective and subjective weight, the prospect decision theory (PDT) performs an imperative role in determining decision‐making problems. PDT predicts human behaviour in terms of gains and losses and considers the expected utility relation to a reference point rather than complete outcomes. In the analysis of merged criteria weight and the prospect decision‐making matrix, the authors get a new dimension level for ranking the array of alternatives. This manuscript provides an improved score function (SF) to convert the interval‐valued membership grades of truth, indeterminacy and falsity into a mathematical and computational value. The advantage of this method is that it merges the objective and subjective weight during the proposed method. They propose an algorithm based on the SF to determine MCDA problems with IVNSSs. They illustrate a case study and provide various comparative analyses to show its significance over existing studies.