概率语言分布视角下的最小调整二分类大群体共识决策方法

A Minimum Adjustment Binary Categorical Large Group Consensus Decision Making Approach from a Probabilistic Linguistic Distribution Perspective

针对考虑实际分类需求的大群体决策问题,文章从偏好信息的概率分布和最小调整视角研究二分类大群体共识决策模型.首先,基于PROMETHEE II方法,通过将数据的概率分布向量与优势度相结合,提出了一种备选方案的初排序方法;其次,根据分类需要,建立二分类选择过程,接着构建了基于最小调整的二分类共识决策优化模型,该模型能够在获取群体共识方案分类需求的同时,最小化决策者的共识调整量;最后,通过一个算例来验证方法的有效性和可行性.

Aiming at the problem of large group decision-making considering the actual classification needs,this paper investigates the binary classification large group consensus decision-making model from a perspective of probability distribution of preference information and minimum adjustment.Firstly,by combining the probability distribution vector of the data with the dominance degree,an alternative ranking method is proposed based on the PROMETHEE II method.Secondly,according to the classification needs,a binary classification selection process is established.Then,a binary classification consensus optimization model based on minimum adjustment is constructed,which can minimize the amount of consensus adjustment of decision makers while obtaining the classification consensus.Finally,a numeral example is used to illustrate the validity and feasibility of the proposed method.