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<p>Multi-criteria decision making (MCDM) plays a crucial role in decision-making and has been widely applied in many industries. However, due to the influence of fuzzy information and irrational behavior, decision-makers often face difficulties in making accurate decisions. To address this issue, this paper proposes a novel MCDM model based on probabilistic uncertain linguistic term sets (PULTSs), Multi-objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Regret Theory (RT). First, a new distance measure method for PULTS is proposed. Next, an additive consistency Best-Worst Method (BWM) is introduced in the PUL environment to obtain subjective weights for the criteria. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method and BWM are then combined to derive the comprehensive criteria weights, balancing both subjectivity and objectivity. Furthermore, the PUL–BWM–CRITIC criteria weight-solving model, along with the PUL–RT–MULTIMOORA alternative ranking model, is used to solve the MCDM problem. Finally, the feasibility of the proposed method is validated through a case study, and its effectiveness is confirmed through sensitivity and comparative analyses.</p>

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A probabilistic uncertain linguistic MCDM model based on regret theory and MULTIMOORA method

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Jianping Fan,
  • Zhuxuan Jin,
  • Meiqing Wu

摘要

Multi-criteria decision making (MCDM) plays a crucial role in decision-making and has been widely applied in many industries. However, due to the influence of fuzzy information and irrational behavior, decision-makers often face difficulties in making accurate decisions. To address this issue, this paper proposes a novel MCDM model based on probabilistic uncertain linguistic term sets (PULTSs), Multi-objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Regret Theory (RT). First, a new distance measure method for PULTS is proposed. Next, an additive consistency Best-Worst Method (BWM) is introduced in the PUL environment to obtain subjective weights for the criteria. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method and BWM are then combined to derive the comprehensive criteria weights, balancing both subjectivity and objectivity. Furthermore, the PUL–BWM–CRITIC criteria weight-solving model, along with the PUL–RT–MULTIMOORA alternative ranking model, is used to solve the MCDM problem. Finally, the feasibility of the proposed method is validated through a case study, and its effectiveness is confirmed through sensitivity and comparative analyses.