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<p>This study aims to overcome the limitations of conventional fuzzy decision-making frameworks in handling multi-attribute information characterized by high levels of uncertainty and interdependence. To achieve this objective, a novel T-spherical linear Diophantine fuzzy set (T-SLDFS) is proposed, which combines the expressive capability of T-spherical fuzzy information with the coupled parameterization of linear Diophantine relations. Based on this concept, the T-SLDF weighted power Heronian mean (T-SLDFWPHM) aggregation operator was constructed to effectively capture the correlations and information heterogeneity among attributes. Furthermore, the CRITIC method is employed to determine objective attribute weights by quantifying contrast intensity and inter-criterion dependency. Subsequently, a hybrid multi-attribute decision-making framework integrating the CRITIC, TODIM, and COCOSO methods is established to evaluate the foreign fiber content levels in textile materials. Numerical and sensitivity analyses verify that the proposed approach exhibits superior ranking stability (within a 5 percent fluctuation range) and enhanced discrimination capability compared with conventional fuzzy aggregation models. The proposed framework offers an interpretable and robust decision-making tool applicable to complex textile evaluation and broader multi-attribute group decision-making contexts.</p>

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A hybrid T-spherical diophantine fuzzy decision framework for evaluating foreign fiber content in textiles

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Xinlong Li,
  • Yuhong Du,
  • Yanzhi Hao,
  • Shuaijie Zhao

摘要

This study aims to overcome the limitations of conventional fuzzy decision-making frameworks in handling multi-attribute information characterized by high levels of uncertainty and interdependence. To achieve this objective, a novel T-spherical linear Diophantine fuzzy set (T-SLDFS) is proposed, which combines the expressive capability of T-spherical fuzzy information with the coupled parameterization of linear Diophantine relations. Based on this concept, the T-SLDF weighted power Heronian mean (T-SLDFWPHM) aggregation operator was constructed to effectively capture the correlations and information heterogeneity among attributes. Furthermore, the CRITIC method is employed to determine objective attribute weights by quantifying contrast intensity and inter-criterion dependency. Subsequently, a hybrid multi-attribute decision-making framework integrating the CRITIC, TODIM, and COCOSO methods is established to evaluate the foreign fiber content levels in textile materials. Numerical and sensitivity analyses verify that the proposed approach exhibits superior ranking stability (within a 5 percent fluctuation range) and enhanced discrimination capability compared with conventional fuzzy aggregation models. The proposed framework offers an interpretable and robust decision-making tool applicable to complex textile evaluation and broader multi-attribute group decision-making contexts.