基于重要度引导偏好识别修正的多粒度语言共识模型

研究多粒度语言偏好信息下的群体共识决策问题. 首先, 从个体和群体两个角度充分挖掘偏好信息下隐含的专家重要度信息, 基于个体一致度及个体与群体的相似度构建确定专家重要度的优化模型; 其次, 以专家重要度引导非共识偏好的识别和修正过程, 提出一种自适应的语言共识模型; 然后, 给出一种群决策方法, 确保在集结专家意见前群体达成一定程度的共识; 最后, 通过算例验证所提出方法的可行性和有效性.

Multi-granularity linguistic consensus model based on preferences identification and correction guided by importance degree

Group consensus decision making problems with multi-granularity linguistic preference information are studied. From two aspects of individual and group, implied importance degree information of experts under preference information is fully exploited, and determined by an optimization model which is constructed based on the individual consistent degree and the similarity degree between individuals and group. An adaptive linguistic consensus model is presented, where non- consensus preferences identification and correction process are guided by experts’ importance degree. A group decision making method which is a guarantee of reaching a certain degree of group consensus before aggregating expert opinions is developed. Finally, a numerical example illustrates the feasibility and effectiveness of the proposed method.