基于云模型和多层权重求解的多粒度语言大群体决策方法
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广东外语外贸大学

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C934

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国家自然科学基金项目


Multi-granularity linguistic large group decision-making based on cloud model and multi-layer weight determination
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Guangdong University of Foreign Studies

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    摘要:

    针对属性权重部分未知且专家权重完全未知的多粒度语言大群体决策问题,提出了一种基于云模型的决策方法。首先构建一种基于信任关系的专家权重求解模型来计算专家权重,然后将多粒度语言转换为云模型并进行聚类,其次构建一致性优化模型来求解属性权重,从而得到各个方案的综合评价值并对方案进行排序。本文方法的特点在于:一方面,所构造的专家赋权模型可以有效解决大群体决策过程中决策人数众多、无法客观给出专家权重信息的问题;在该模型中,通过定义的直觉信任函数,还可以对专家之间的信任关系进行刻画,充分挖掘专家之间的信息。另一方面,将多粒度语言转换为云模型,可以有效刻画语言信息的模糊性和随机性,从而避免信息的丢失和失真。

    Abstract:

    For a large group decision-making problem with partly known attribute weights and completely unknown expert weights, a new decision-making method based on the cloud model is proposed. Firstly, an expert weight determination model based on the trust network is established to obtain the weight information of each expert. Secondly, the linguistic preferences with different multi-granularity are transformed into clouds, and then a clustering process is applied. Thirdly, an optimization model is constructed to compute the attribute weights, and then the comprehensive evaluation value for each alternative is presented and the ranking results can be derived. The features of the proposed method are as follows: on the one hand, the expert weight determination model can solve the decision making problems where there exist a large number of experts and the weight information of each expert is difficult to be provided objectively. In addition, the defined intuitionistic trust set is a good way to describe the trust network among experts, which can help to exploit the information of experts. On the other hand, by transforming the multi-granularity linguistic variables into clouds, it can describe the fuzziness and randomness of linguistic information and avoid information losses and distortions.

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历史
  • 收稿日期:2020-02-02
  • 最后修改日期:2020-05-11
  • 录用日期:2020-05-12
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