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Research Article

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

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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.

摘要

纯棉面料的选择是服装产品设计和开发过程中一项具有挑战性的任务,在多个决策标准存在的情况下,最优方案的选择变得复杂,因此被认为是一个多标准决策问题. 此外,选择过程还涉及到模糊性和不确定性. 在本研究中,引入勾股模糊集(PFSs)来处理不确定信息. 消除和选择翻译现实(ELECTRE)是解决MCDM问题的一种众所周知的高级方法. 因此,我们在PFS环境下扩展了ELECTRE方法,并提出了一种基于相关性的贴近度系数来比较勾股模糊数. 本文将所提出的PF-ELECTRE方法应用于解决一个涉及棉织物分级的实际案例. 为了展示所建议方法的优越性和稳健性,进行了灵敏度分析,以检查权重变化的影响,并在PF-ELECTRE与几种现有的MCDM方法之间进行了比较分析. 这项研究通过一种利用加权相关系数的新型PF-ELECTRE方法,为MCDM方法的进步和发展做出了贡献. 此外,所开发的方法可以获得可靠的结果,并可用于其他纺织领域.

Acknowledgements

The authors acknowledge the assistance of the respected editor and the anonymous referees for their insightful and constructive comments, which helped to improve the overall quality of the paper. The corresponding author is grateful for grant funding support from the National Science and Technology Council, Taiwan (NSTC 111-2410-H-182-012-MY3) and Chang Gung Memorial Hospital, Linkou, Taiwan (BMRP 574) during the completion of this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Compliance with ethical standards

This article does not contain any studies with human participants or animals that were performed by any of the authors.

Additional information

Funding

The work was supported by the National Science and Technology Council, Taiwan [NSTC 111-2410-H-182-012-MY3]; Chang Gung Memorial Hospital, Linkou, Taiwan [BMRP 574].