Selection of Knitted Fabrics Using a Hybrid BBWM-PFTOPSIS Method

ABSTRACT

Selecting the best knitted fabric with various comfort properties is considered a complicated multi-criteria decision-making (MCDM) issue that involves ambiguity and vagueness. In such scenarios, Pythagorean fuzzy sets (PFSs) provide an effective tool for addressing uncertainty and ambiguity in MCDM problems that contain human subjective evaluations and judgments. First, this research identifies the factors affecting the comfort of knitted fabrics as the evaluation criteria. Second, the Bayesian best-worst method (BBWM) is preferred for less pairwise comparisons and obtains highly reliable results with a probabilistic perspective for determining the criteria weights. Furthermore, due to its logical computation approach and ease of operation, the technique for order preference by similarity to ideal solution (TOPSIS) is commonly utilized for addressing MCDM problems. Therefore, this research proposes an innovative MCDM framework that combines the BBWM technique with Pythagorean fuzzy TOPSIS (PFTOPSIS). The BBWM determines the criteria weights, and the weighted sine similarity-based PFTOPSIS is utilized to rank alternatives. The proposed BBWM-PFTOPSIS approach was employed to solve a real-world case. Moreover, this article conducts a sensitivity analysis and three comparative analyses to reveal the efficiency and reliability of the BBWM-PFTOPSIS approach. The ranking results establish the viability and effectiveness of BBWM-PFTOPSIS.

摘要

选择具有各种舒适性能的最佳针织物被认为是一个复杂的多准则决策问题，涉及模糊性和模糊性. 在这种情况下，勾股模糊集（PFSs）为解决包含人类主观评价和判断的MCDM问题中的不确定性和模糊性提供了一个有效的工具. 首先，本研究确定了影响针织物舒适性的因素作为评价标准. 其次，贝叶斯最佳-最差方法（BBWM）对于较少的成对比较是优选的，并且从概率的角度获得了用于确定标准权重的高度可靠的结果. 此外，由于其逻辑计算方法和易操作性，通过与理想解的相似性进行排序偏好的技术（TOPSIS）通常用于解决MCDM问题. 因此，本研究提出了一种创新的MCDM框架，将BBWM技术与勾股模糊TOPSIS（PFTOPSIS）相结合. BBWM确定标准权重，并利用基于加权正弦相似性的PFTOPSIS对备选方案进行排序. 所提出的BBWM-PFTOPSIS方法被用于解决真实世界的案例. 此外，本文还进行了敏感性分析和三次比较分析，以揭示BBWM-PFTOPSIS方法的有效性和可靠性. 排名结果确定了BBWM-PFTOPSIS的可行性和有效性.

KEYWORDS:

• Knitted fabrics
• Multi-criteria decision-making (MCDM)
• Pythagorean fuzzy set (PFS)
• Bayesian best-worst method (BBWM)
• Technique for order preference by similarity to ideal solution (TOPSIS)
• BBWM-PFTOPSIS

关键词:

• 针织面料
• 多准则决策
• 勾股模糊集 (PFS)
• 贝叶斯最佳-最差方法 (BBWM)
• 基于理想解相似性的排序偏好技术 （TOPSIS）

Highlights

• Utilize the Bayesian best-worst method (BBWM) for criteria weight calculation.

• Introduce PF-TOPSIS, an extension of TOPSIS method, tailored for PF environment.

• Develop the weighted sine similarity-based PFTOPSIS to rank alternative options.

• Propose a hybrid BBWM-PFTOPSIS approach to select knitted fabrics.

• Conduct sensitivity and comparative analyses to assess the method’s feasibility.

Acknowledgements

The authors acknowledge the assistance of the respected editor and the anonymous referees for their insightful and constructive comments, which helped 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 authors.

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