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<p>Multi-criteria group decision-making (MCGDM) problems in real-world environments are often characterized by high dimensionality, uncertainty, heterogeneous expert opinions, and varying levels of expert credibility. Existing fuzzy MCDM approaches primarily rely on matrix-based representations and typically incorporate expert credibility as external weights, which limits their ability to capture complex multi-expert interactions and traceability of influence. To overcome these limitations, this study proposes a Fuzzy Credibility Tensor (FCT) framework as a methodological advancement in MCGDM. Unlike conventional extensions of fuzzy MCDM, the proposed approach models decision information as a high-order tensor in which each tensor entry simultaneously encodes fuzzy membership values and expert credibility. This tensorial representation enables the unified and intrinsic integration of alternatives, criteria, experts, and credibility dimensions within a single mathematical structure. Based on the proposed FCT framework, a novel credibility-aware group decision-making algorithm is developed, incorporating tensor-based aggregation, dimensional contraction, and credibility-weighted normalization. The methodology preserves expert influence traceability, enhances robustness against unreliable judgments, and supports flexible aggregation through <i>t</i>-norms, <i>t</i>-conorms, and weighted operators. The effectiveness of the proposed MCGDM framework is demonstrated through a renewable energy project selection problem involving multiple alternatives, criteria, and decision-makers. Sensitivity analysis confirms the stability of the ranking results under variations in criteria weights and expert credibility. Comparative analysis with existing fuzzy and credibility-based MCDM methods highlights the superiority of the proposed approach in handling high-dimensional, credibility-sensitive group decision environments. The proposed FCT-based MCGDM framework provides a rigorous, transparent, and scalable decision-support tool for complex applications such as energy planning, healthcare evaluation, and large-scale infrastructure assessment.</p>

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A credibility-weighted fuzzy tensor approach for high-dimensional group decision-making with application to renewable energy project selection

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Muhammad Bilal,
  • Li Chaoqian,
  • A. K. Alzahrani,
  • A. K. Aljahdali

摘要

Multi-criteria group decision-making (MCGDM) problems in real-world environments are often characterized by high dimensionality, uncertainty, heterogeneous expert opinions, and varying levels of expert credibility. Existing fuzzy MCDM approaches primarily rely on matrix-based representations and typically incorporate expert credibility as external weights, which limits their ability to capture complex multi-expert interactions and traceability of influence. To overcome these limitations, this study proposes a Fuzzy Credibility Tensor (FCT) framework as a methodological advancement in MCGDM. Unlike conventional extensions of fuzzy MCDM, the proposed approach models decision information as a high-order tensor in which each tensor entry simultaneously encodes fuzzy membership values and expert credibility. This tensorial representation enables the unified and intrinsic integration of alternatives, criteria, experts, and credibility dimensions within a single mathematical structure. Based on the proposed FCT framework, a novel credibility-aware group decision-making algorithm is developed, incorporating tensor-based aggregation, dimensional contraction, and credibility-weighted normalization. The methodology preserves expert influence traceability, enhances robustness against unreliable judgments, and supports flexible aggregation through t-norms, t-conorms, and weighted operators. The effectiveness of the proposed MCGDM framework is demonstrated through a renewable energy project selection problem involving multiple alternatives, criteria, and decision-makers. Sensitivity analysis confirms the stability of the ranking results under variations in criteria weights and expert credibility. Comparative analysis with existing fuzzy and credibility-based MCDM methods highlights the superiority of the proposed approach in handling high-dimensional, credibility-sensitive group decision environments. The proposed FCT-based MCGDM framework provides a rigorous, transparent, and scalable decision-support tool for complex applications such as energy planning, healthcare evaluation, and large-scale infrastructure assessment.