A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets

Sen Liu, Wei Yu, Felix T.S. Chan, Ben Niu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)

Abstract

This article aims to develop a novel hybrid multi-attribute group decision-making approach under interval-valued intuitionistic fuzzy sets (IVIFS) by integrating variable weight, correlation coefficient, and technique for order performance by similarity to an ideal solution (TOPSIS). First, experts give their evaluation in IVIFS, and then the weighting evaluation matrix is computed based on interval-valued intuitionistic fuzzy weighted averaging operator with the subjective attribute weights given in advance. Second, a simple and useful weighting approach on the basis of correlation coefficient is put forward to obtain the experts weights. Third, we treat the attribute weights as a varying vector, and then propose a variable weighting approach for its acquisition. Fourth, an individual decision can be converted to an alternative decision by considering the experts and attributes weights together. At last, the integrated assessment value of each alternative is computed by TOPSIS, and then the most appropriate alternative is chosen. Two illustrative examples dealt with the problem by the method presented in this article demonstrate the usefulness of this approach, compared with those by the other methods.

Original languageEnglish
Pages (from-to)1015-1052
Number of pages38
JournalInternational Journal of Intelligent Systems
Volume36
Issue number2
DOIs
Publication statusPublished - Feb 2021

Keywords

  • interval-valued intuitionistic fuzzy sets
  • multi-attribute group decision making
  • TOPSIS
  • variable weight

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Cite this