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An Improved Consensus-Based Model for Large Group Decision Making Problems Considering Experts with Linguistic Weighted Information

机译:考虑语言加权信息专家的大型群决策基于综合型号的改进

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摘要

In this paper, a consensus-based model in a large group decision making environment is proposed to achieve the group consensus among experts. This framework considers experts with different importance levels, which are expressed by the forms of linguistic variables. More specifically, the group of large-scale experts is firstly transferred into some small groups to simplify the decision making process. Following this, cluster weights can be determined by combining the group size of a cluster and the importance degrees of experts in a cluster. In the consensus process, an objective adjustment coefficient based on the expert's importance degree is developed to modify the individual preferences, which can retain the original decision information of expert with higher importance degree as much as possible. The main characteristic of the proposed objective adjustment coefficient is that generates different coefficients for different clusters based on experts' importance degrees. Finally, the proposed model is applied to an application example, and some detailed analysis and comparisons are given to verify the feasibility and the effectiveness of the proposed method.
机译:本文提出了一个基于共识的大型决策环境中的模型,以实现专家之间的团体共识。该框架考虑了具有不同重要性水平的专家,这些专家由语言变量的形式表示。更具体地说,大型专家组首先转入一些小组,以简化决策过程。在此之后,可以通过组合集群的组大小和集群中的重要专家的群集大小来确定群集权重。在共识过程中,开发了基于专家重要性程度的客观调整系数,以修改个性偏好,可以尽可能更高的重要程度保留专家的原始决策信息。所提出的客观调整系数的主要特征是基于专家的重要性为不同集群产生不同系数。最后,所提出的模型应用于应用示例,并提供了一些详细的分析和比较来验证所提出的方法的可行性和有效性。

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