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<p>Multi-criteria decision making (MCDM) is used to rank multiple alternatives with multiple conflicting criteria, which is widely used in management. Three-way decision (TWD) is a model based on decision rough sets, which ranks all alternatives by Bayesian risk theory under multi-criteria decision environment. However, TWD has not been widely used in practice due to its dependence on the additional information, such as the conditional probabilities of the alternatives, the weighting scheme, and the risk preferences of the decision makers. The main motivation of this paper is to introduce the data-driven approaches to calculate the additional information required for TWD, thus, avoiding the burden of additional information on the decision maker and the decision bias of the subjective decision information. Specifically, we improve the removal effects of criterion method under the picture fuzzy environment to calculate the weighting scheme, and develop a picture fuzzy evidential reasoning approach to calculate the conditional probability in the proposed decision framework. Moreover, a maximum entropy principle is proposed to search for the optimal risk avoidance coefficients, which avoids decision errors caused by subjective selection of coefficients. Finally, the rationality and robustness of the proposed method are demonstrated through comparative experiments and sensitivity analysis.</p>

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Three-Way Decision Based on Removal Effects of Criteria and Evidential Reasoning Approach Under Picture Fuzzy Multi-criteria Environment

  • 期刊论文

  • 发表日期：

    2025年2月19日

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  • Jiang Li,
  • Jianping Fan,
  • Ruixin Chen,
  • Meiqin Wu

摘要

Multi-criteria decision making (MCDM) is used to rank multiple alternatives with multiple conflicting criteria, which is widely used in management. Three-way decision (TWD) is a model based on decision rough sets, which ranks all alternatives by Bayesian risk theory under multi-criteria decision environment. However, TWD has not been widely used in practice due to its dependence on the additional information, such as the conditional probabilities of the alternatives, the weighting scheme, and the risk preferences of the decision makers. The main motivation of this paper is to introduce the data-driven approaches to calculate the additional information required for TWD, thus, avoiding the burden of additional information on the decision maker and the decision bias of the subjective decision information. Specifically, we improve the removal effects of criterion method under the picture fuzzy environment to calculate the weighting scheme, and develop a picture fuzzy evidential reasoning approach to calculate the conditional probability in the proposed decision framework. Moreover, a maximum entropy principle is proposed to search for the optimal risk avoidance coefficients, which avoids decision errors caused by subjective selection of coefficients. Finally, the rationality and robustness of the proposed method are demonstrated through comparative experiments and sensitivity analysis.