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<p>The selection of Construction Land Reduction (CLR) schemes is a critical multi-attribute decision-making (MADM) problem, which involves evaluating and comparing various land use plans to identify the most effective strategies for sustainable land management. However, existing methods often overlook the interactions among attributes and the uncertainty inherent in expert evaluations. To address these challenges, this paper proposes an Intuitionistic Fuzzy Mahalanobis–Taguchi System (IF-MTS) approach for interactive MADM in CLR projects. Specifically, we extend the Mahalanobis–Taguchi System to an intuitionistic fuzzy environment, introducing the Intuitionistic Fuzzy Mahalanobis Distance (IF-MD) to capture correlations between attributes. We subsequently integrate the 2-additive fuzzy measure to model attribute interactions and compute Shapley values for determining weights. The proposed method is applied to a real-world case study in Baoshan District, Shanghai, where five alternative CLR schemes are evaluated against sustainability criteria. The results demonstrate that the IF-MTS method effectively handles attribute interactions and uncertainty, leading to more reliable and robust decision outcomes. This study not only provides a practical tool for CLR scheme selection but also offers a generic framework for addressing interactive fuzzy MADM problems in other fields, such as environmental management and urban planning.</p>

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Interactive Multi-Attribute Decision-Making in Construction Land Reduction: An Intuitionistic Fuzzy Mahalanobis–Taguchi Approach

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Bingxu Mu,
  • Yuming Zhu,
  • Mingzhen Zhang,
  • Lei He,
  • Xin Zheng

摘要

The selection of Construction Land Reduction (CLR) schemes is a critical multi-attribute decision-making (MADM) problem, which involves evaluating and comparing various land use plans to identify the most effective strategies for sustainable land management. However, existing methods often overlook the interactions among attributes and the uncertainty inherent in expert evaluations. To address these challenges, this paper proposes an Intuitionistic Fuzzy Mahalanobis–Taguchi System (IF-MTS) approach for interactive MADM in CLR projects. Specifically, we extend the Mahalanobis–Taguchi System to an intuitionistic fuzzy environment, introducing the Intuitionistic Fuzzy Mahalanobis Distance (IF-MD) to capture correlations between attributes. We subsequently integrate the 2-additive fuzzy measure to model attribute interactions and compute Shapley values for determining weights. The proposed method is applied to a real-world case study in Baoshan District, Shanghai, where five alternative CLR schemes are evaluated against sustainability criteria. The results demonstrate that the IF-MTS method effectively handles attribute interactions and uncertainty, leading to more reliable and robust decision outcomes. This study not only provides a practical tool for CLR scheme selection but also offers a generic framework for addressing interactive fuzzy MADM problems in other fields, such as environmental management and urban planning.