An approach for fuzzy group decision making and consensus measure with hesitant judgments of experts

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chao Huang, Xiaoyue Wu, Mingwei Lin, Zeshui Xu
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Abstract

In some actual decision-making problems, experts may be hesitant to judge the performances of alternatives, which leads to experts providing decision matrices with incomplete information. However, most existing estimation methods for incomplete information in group decision-making (GDM) neglect the hesitant judgments of experts, possibly making the group decision outcomes unreasonable. Considering the hesitation degrees of experts in decision judgments, an approach is proposed based on the triangular intuitionistic fuzzy numbers (TIFNs) and TODIM (interactive and multiple criteria decision-making) method for GDM and consensus measure. First, TIFNs are applied to handle incomplete information due to the hesitant judgments of experts. Second, considering the risk attitudes of experts, a decision-making model is proposed to rank alternatives for GDM with incomplete information. Subsequently, based on measuring the concordance between solutions, a consensus model is presented to measure the group’s and individual’s consensus degrees. Finally, an illustrative example is presented to show the detailed implementation procedure of the proposed approach. The comparisons with some existing estimation methods verify the effectiveness of the proposed approach for handling incomplete information. The impacts and necessities of experts’ hesitation degrees are discussed by a sensitivity analysis.

Abstract Image

专家犹豫不决的模糊群体决策和共识度量方法
在一些实际决策问题中,专家可能会对备选方案的性能判断犹豫不决,从而导致专家提供的决策矩阵信息不完整。然而,现有的群体决策(GDM)不完全信息估计方法大多忽视了专家的犹豫判断,可能会使群体决策结果不合理。考虑到专家在决策判断中的犹豫程度,本文提出了一种基于三角直觉模糊数(TIFNs)和 TODIM(交互式多准则决策)方法的 GDM 和共识度量方法。首先,三角直觉模糊数用于处理由于专家判断犹豫不决而导致的信息不完整问题。其次,考虑到专家的风险态度,提出了一个决策模型,用于对不完整信息下的 GDM 备选方案进行排序。随后,在测量解决方案之间一致性的基础上,提出了一个共识模型来测量群体和个人的共识度。最后,通过一个示例展示了所提方法的具体实施过程。通过与一些现有估算方法的比较,验证了所提方法在处理不完整信息方面的有效性。通过敏感性分析讨论了专家犹豫度的影响和必要性。
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来源期刊
Knowledge and Information Systems
Knowledge and Information Systems 工程技术-计算机:人工智能
CiteScore
5.70
自引率
7.40%
发文量
152
审稿时长
7.2 months
期刊介绍: Knowledge and Information Systems (KAIS) provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This monthly peer-reviewed archival journal publishes state-of-the-art research reports on emerging topics in KAIS, reviews of important techniques in related areas, and application papers of interest to a general readership.
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