Herrera-Viedma Enrique, Chiclana Francisco, Herrera Francisco, Alonso Sergio
Department of Computer Science and Artificial Intelligence, University of Granada, Spain.
IEEE Trans Syst Man Cybern B Cybern. 2007 Feb;37(1):176-89. doi: 10.1109/tsmcb.2006.875872.
In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree.
在决策问题中，可能存在专家对要解决的问题没有深入了解的情况。在这种情况下，专家可能不会就问题的某些方面提出自己的意见，结果他们可能会给出不完整的偏好，即某些偏好值可能未给出或缺失。在本文中，我们提出了一种新的群体决策模型，其中专家的偏好可以表示为不完整的模糊偏好关系。作为该决策模型的一部分，我们提出了一种迭代程序来估计专家不完整模糊偏好关系中的缺失信息。该程序以加法一致性（AC）属性为指导，并且仅使用专家提供的偏好值。AC属性还用于衡量专家提供的信息的一致性水平，并提出一种新的诱导有序加权平均（IOWA）算子，即AC - IOWA算子，它允许以更重视最一致偏好的方式汇总专家的偏好。最后，根据专家的模糊多数来选择备选方案的解集基于两个量词引导的选择度：优势度和非优势度。
