Adjustable approaches to multi-criteria group decision making based on inverse fuzzy soft matrices

Petchimuthu S., KAMACI H.

SCIENTIA IRANICA, cilt.29, sa.4, ss.2166-2190, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.24200/sci.2020.54294.3686
  • Dergi Adı: SCIENTIA IRANICA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Arab World Research Source, Communication Abstracts, Compendex, Geobase, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2166-2190
  • Anahtar Kelimeler: Fuzzy sets, Inverse fuzzy soft sets, Inverse fuzzy soft matrices, Operations of inverse fuzzy soft matrices, Multi-criteria group decision making, DEMPSTER-SHAFER THEORY, ROW-PRODUCTS, SET-THEORY
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

In this paper, we focus on the matrices representing the inverse fuzzy soft sets over both the universal object set and the universal parameter set. Some basic operations and properties of these inverse fuzzy soft matrices were investigated. Moreover, two adjustable approaches to Multi-Criteria Group Decision Making (MCGDM), namely Inverse Fuzzy Soft Sum-Product Decision Making (IFSSPDM) and Inverse Fuzzy Soft Distributive If-difference Decision Making (IFSDIf-dDM), were developed. The IFSSPDM approach achieved the optimal choice for the MCGDM problem based on the inverse fuzzy soft structures consisting of multiple-discrete parameter sets and common universal object sets. The objective of IFSDIf-dDM approach was to present a solution to the MCGDM problem based on the inverse fuzzy soft structures consisting of a common universal parameter set and two discrete universal object sets. Thus, the solutions could be obtained using the practicality of inverse fuzzy soft matrices for two different types of decision making problems. Besides, the comparisons made showed that the proposed approaches produced more convincing outputs than the current fuzzy soft approaches. (C) 2022 Sharif University of Technology. All rights reserved.