A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment

• Published in: Information fusion Vol. 23; pp. 156 - 165
• Main Authors: Liu, Bingsheng, Shen, Yinghua, Chen, Yuan, Chen, Xiaohong, Wang, Yumeng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2015
• Subjects: 2-Tuple linguistic (2TL) representation model; Complex multi-attribute large-group decision-making (CMALGDM); Expert weight determination; Interval-valued 2-tuple linguistic (IV2TL) representation model; Complex multi-attribute large-group decision-making (CMALGDM); Expert weight determination; 2-Tuple linguistic (2TL) representation model; Interval-valued 2-tuple linguistic (IV2TL) representation model
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2014.05.001

We propose a two-layer weight determination model in a linguistic environment, when all the clustering results of the experts are known, to objectively obtain expert weights in complex multi-attribute large-group decision-making (CMALGDM) problems. The linguistic information considered in this paper involves both linguistic terms and linguistic intervals. We assume that, for CMALGDM problems, the final expert weights should be determined based on the expert weight in the cluster and on the cluster weights. This is mainly because experts in the same cluster will certainly make varying contributions to the cluster’s overall consensus, and different clusters will also obtain the distinctive “cluster information quality”. Hence, a Minimized Variance Model and an Entropy Weight Model are proposed to determine the expert weights in the cluster and the cluster weights, respectively. We then synthesize these two types of weights into the final objective weights of the CMALGDM experts. The feasibility of the two-layer weight determination model method for the CMALGDM problems is illustrated using a case study of salary reform for professors at a university.