多标准决策问题的比例模糊消除和选择转换现实 II 方法:适用于实际应用的稳健新方法

Jing Guo, Xianjun Zhu, Kun You, Zhenzhen Wang, Qianqian Wang, Hui Li, Xianzhong Zhou
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摘要

由于实际问题的复杂性和知识的局限性,决策者(DMs)往往很难提供准确的信息。如何准确、有效地传递决策者的评价信息影响着多标准决策(MCDM)。因此,与精确数字相比,语言上犹豫不决的模糊集可以更灵活、更接近人类思维地表达人们的评价,尤其是在高度复杂的决策过程中。本文受犹豫模糊集的启发,提出了比例区间项集(PITS)来表示更重要的不确定性。为了更有效地比较两个 PITS,每个集合中的元素数量必须相同。因此,为此提出了 PITS 的标准化。标准化提高了两个 PITS 的距离和可能性度。此外,考虑到对称区间项集在处理不确定信息方面更重要的能力,提出了一种综合消除和选择转换现实(ELECTRE)II 方法来解决 MCDM 问题。第一阶段汇总专家对每个备选方案和标准的 PITS 意见,并借助距离理论确定标准的权重。然后,利用可能性度,该方法引入了三个 PITS 排序集(一致集、冷漠集和不一致集),并定义了强弱排序关系。然后,利用优势矩阵对备选方案进行排序。最后,应用试点研究可以富有成效地证明和标志拟议决策方法的实用性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Proportional Fuzzy Elimination and Choice Translating Reality II Method for Multicriteria Decision-Making Problems: A Robust New Methodology for Practical Applications
As a result of the complexity of practical problems and the limitations of knowledge, it is often tricky for decision-makers (DMs) to give accurate information. How to accurately and effectively convey the evaluation information of DMs affects multicriteria decision-making (MCDM). Therefore, linguistically hesitant fuzzy sets can express people’s evaluations with greater flexibility and resemblance to human thought than exact numbers, particularly during highly complex decision-making processes. This article proposes the proportional interval term set (PITS) inspired by hesitant fuzzy sets to indicate more significant uncertainty. To more effectively compare two PITSs, the number of elements in each set must be identical. So the standardization of the PITS is presented for this purpose. The standardization improves the distance and possibility degree of two PITSs. Moreover, considering the more vital capability of the symmetrical interval term set to handle uncertain information, an integrated elimination and choice translating reality (ELECTRE) II method for addressing MCDM issues is proposed. The first phase aggregates the PITS opinions of experts on each alternative and criterion and determines the weights of the criteria with the aid of distance theory. Then, using possibility degrees, the method introduces three PITS outranking sets (concordance, indifferent, and discordance sets) and defines strong and weak outranking relations. Further, a dominance matrix ranks alternatives. Finally, applying the pilot study can fruitfully demonstrate and signify the practicality and feasibility of the proposed decision-making approach.
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