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<p>This study addresses the escalating complexity of multi-attribute group decision-making (MAGDM) by incorporating decision-makers’ psychological preferences into uncertain environments. A novel MAGDM framework is developed to navigate heterogeneous fuzzy information through the synergistic application of bidirectional projection and set pair theory for information isomorphization. By integrating cumulative prospect theory, the proposed model effectively captures the asymmetrical perception of gains and losses, while attribute weights are dynamically optimized via the entropy weight method and nonlinear correction functions. The efficacy of the method is validated through a case study on dry-hot rock site selection, supported by comprehensive sensitivity, comparative, and statistical analyses. The results demonstrate that the proposed framework yields robust and stable alternative rankings that align closely with practical engineering imperatives. Overall, this research provides a scientifically rigorous and adaptable decision-support tool for complex energy infrastructure projects.</p>

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Methods for multi-attribute group decision-making based on decision preferences in heterogeneous fuzzy information environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Siqi Ren,
  • Yuhong Wang

摘要

This study addresses the escalating complexity of multi-attribute group decision-making (MAGDM) by incorporating decision-makers’ psychological preferences into uncertain environments. A novel MAGDM framework is developed to navigate heterogeneous fuzzy information through the synergistic application of bidirectional projection and set pair theory for information isomorphization. By integrating cumulative prospect theory, the proposed model effectively captures the asymmetrical perception of gains and losses, while attribute weights are dynamically optimized via the entropy weight method and nonlinear correction functions. The efficacy of the method is validated through a case study on dry-hot rock site selection, supported by comprehensive sensitivity, comparative, and statistical analyses. The results demonstrate that the proposed framework yields robust and stable alternative rankings that align closely with practical engineering imperatives. Overall, this research provides a scientifically rigorous and adaptable decision-support tool for complex energy infrastructure projects.