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<p>With the rising demand for high-quality textiles, efficiently identifying and removing foreign fibers from cotton has become a critical issue in quality management. Multi-attribute group decision-making (MAGDM) methods are effective tools for evaluating foreign fiber content, as they comprehensively account for diverse characteristics. As an extension of fuzzy sets, T-spherical fuzzy sets (TSFs) enable a more comprehensive representation of uncertainty in MAGDM. This study proposes a T-spherical fuzzy projection-based WASPAS (TSF-PR-WASPAS) method grounded in a stereo-projection model. First, the Power Bonferroni Mean (PBM) operator is extended to TSFs, and a novel TSFPBM operator is developed to address unreasonable attribute values and incorporate inter-attribute interactions. The expert membership matrix projection and the CRITIC method are employed to determine attribute weights, thereby enhancing the rationality of the weighting scheme. The practicality of the proposed method is illustrated through a case study on foreign fiber evaluation. The TSF-PR-WASPAS approach comprehensively accounts for decision-maker weights, attribute importance, and extreme values, thereby offering a more rigorous decision-making framework. This method serves as a valuable reference for addressing complex uncertainty and extending fuzzy decision-making models to other application domains.</p>

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CRITIC-WASPAS method for multi-attribute group decision-making based on T-spherical fuzzy projection model and its application to foreign fiber content grade evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Xinlong Li,
  • Yuhong Du,
  • Ziqi Rong,
  • Weijia Ren

摘要

With the rising demand for high-quality textiles, efficiently identifying and removing foreign fibers from cotton has become a critical issue in quality management. Multi-attribute group decision-making (MAGDM) methods are effective tools for evaluating foreign fiber content, as they comprehensively account for diverse characteristics. As an extension of fuzzy sets, T-spherical fuzzy sets (TSFs) enable a more comprehensive representation of uncertainty in MAGDM. This study proposes a T-spherical fuzzy projection-based WASPAS (TSF-PR-WASPAS) method grounded in a stereo-projection model. First, the Power Bonferroni Mean (PBM) operator is extended to TSFs, and a novel TSFPBM operator is developed to address unreasonable attribute values and incorporate inter-attribute interactions. The expert membership matrix projection and the CRITIC method are employed to determine attribute weights, thereby enhancing the rationality of the weighting scheme. The practicality of the proposed method is illustrated through a case study on foreign fiber evaluation. The TSF-PR-WASPAS approach comprehensively accounts for decision-maker weights, attribute importance, and extreme values, thereby offering a more rigorous decision-making framework. This method serves as a valuable reference for addressing complex uncertainty and extending fuzzy decision-making models to other application domains.