Development of the ELECTRE Method Under Pythagorean Fuzzy Sets Based on Existing Correlation Coefficients for Cotton Fabric Selection

Jing Ye, Ting Yu Chen*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

4 Scopus citations

Abstract

Cotton fabric selection is a challenging task in the garment product design and development process, and the selection of optimal alternative under the presence of multiple decision criteria becomes complex, and hence it is considered as a multi-criteria decision-making (MCDM) problem. In addition, the selection process involves fuzziness and uncertainty. In this study, Pythagorean fuzzy sets (PFSs) are introduced to handle uncertain information. Elimination and choice translating reality (ELECTRE) is a well-known outranking method for solving MCDM problems. Therefore, we extend the ELECTRE method under the PFS environment, and a correlation-based closeness coefficient is proposed to compare Pythagorean fuzzy numbers (PFNs). This paper applies the proposed PF-ELECTRE approach in solving a practical case involving the ranking cotton fabrics. To exhibit the superiority and robustness of the suggested method, sensitivity analysis is performed to examine the impacts of weights variation, as well as a comparative analysis is carried out between the PF-ELECTRE with several existing MCDM methods. The research contributes to the advancement and development of outranking MCDM methods through a novel PF-ELECTRE approach that utilizes the weighted correlation coefficient. Moreover, the developed method can obtain reliable results and can be used to other textile domains.

Original languageEnglish
Article number2201486
JournalJournal of Natural Fibers
Volume20
Issue number2
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

Keywords

  • Cotton fabric selection
  • ELECTRE
  • Pythagorean fuzzy sets
  • comparative analysis
  • correlation coefficient
  • multi-criteriadecision-making

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