面向犹豫模糊语言信息的大型群体分类集结模型

研究基于犹豫模糊语言信息的大型群体决策分类和集结问题.提出一种面向犹豫模糊语言信息的专家相似度构建方法,相似度测算基于犹豫相似度和距离相似度综合考虑;改进编网分类方法,借助基于相似矩阵的编网分类方法对大型群体专家进行初步分类,确定可接受范围,对专家进行再分类并通过分类精度指标对分类的有效性进行验证以确定最终类别;构建大规模群体信息集结的类内集结和类间集结框架,对类内专家信息进行集结以获得概率语言信息;提出一种基于语言概率分布的类可靠度计算方法,基于类可靠度和类专家数量占总体数量比例综合考虑确定类别权重以实现类间信息集结,进而根据集结的概率语言信息计算对象期望值并进行排序.最后,通过算例及方法比较验证所提出方法的有效性.

Classification-based aggregation model on large scale group decision making with hesitant fuzzy linguistic information

A classification and aggregation problem on large-scale group decision making is studied based on the hesitant fuzzy linguistic terms. Specifically, a method to measure the similarity of two hesitant linguistic sets is proposed considering both hesitancy and distance. Then, the preliminary expert classes are generated using the netting method based on a similarity matrix, and an acceptable level is set to help make a second classification. Through a classification accuracy index, the final classes are obtained. Furthermore, two aggregating frames are constructed respectively for the information within one class and between the classes. The proportional linguistic groups are obtained through combing the information within one class. On that basis, the degree of reliability of each class is calculated and the class weights are determined based on the class reliability and the percentage of expert number in one class to the total number to aggregate the information among classes. Additionally, the expected values of the alternatives are calculated to make a selection. Finally, a case is given to illustrate the effectiveness and feasibility of the proposed method.