A consensus model for group decision making with incomplete fuzzy preference relations
A consensus model for group decision making with incomplete fuzzy preference relations
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DOI：
10.1109/tfuzz.2006.889952
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发表时间：
2007-10-01
影响因子：
11.9
通讯作者：
Herrera, Francisco
中科院分区：
文献类型：
--
作者：
Herrera-Viedma, Enrique;Alonso, Sergio;Herrera, Francisco
解决群体决策问题需要两个过程：共识过程和选择过程。达成共识的过程是获得专家之间一定程度的一致的最终解决方案所必需的；而选择过程是获得这样一个最终解的必要条件。在之前的文章中，我们提出了一种处理不完全模糊偏好关系的群体决策问题的选择过程，该过程使用一致性度量来估计不完全模糊偏好关系。本文提出了一个共识模型。这种共识模型的主要新颖之处在于，它同时受到共识和一致性度量的指导。此外，达成共识的过程是自动引导的，没有主持人，通过共识和一致性标准。为此，开发了一种反馈机制，就专家应该如何改变或完成他们的偏好产生建议，以便达成高度共识和一致性的解决方案。在每一轮共识中，专家被告知如何改变他们的偏好，如果他们相应的偏好关系是不完整的，他们就会估计缺失的值。此外，引入了一个基于共识和一致性的诱导有序加权平均算子来汇总专家的偏好，该算子可用于共识模型和选择过程。这个共识模型的主要改进在于它支持对不完全信息的管理，并且它允许在很大程度上达成一致的解决方案。
Two processes are necessary to solve group decision making problems: A consensus process and a selection process. The consensus reaching process is necessary to obtain a final solution with a certain level of agreement between the experts; and the selection process is necessary to obtain such a final solution. In a previous paper, we present a selection process to deal with group decision making problems with incomplete fuzzy preference relations, which uses consistency measures to estimate the incomplete fuzzy preference relations. In this paper we present a consensus model. The main novelty of this consensus model is that of being guided by both consensus and consistency measures. Also, the consensus reaching process is guided automatically, without moderator, through both consensus and consistency criteria. To do that, a feedback mechanism is developed to generate advice on how experts should change or complete their preferences in order to reach a solution with high consensus and consistency degrees. In each consensus round, experts are given information on how to change their preferences, and to estimate missing values if their corresponding preference relation is incomplete. Additionally, a consensus and consistency based induced ordered weighted averaging operator to aggregate the experts' preferences is introduced, which can be used in consensus models as well as in selection processes. The main improvements of this consensus model is that it supports the management of incomplete information and it allows to achieve consistent solutions with a great level of agreement.