Paper Info
Title
A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment.
Abstract
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.
Year
DOI
Venue
2015
10.1016/j.inffus.2014.05.001
Information Fusion
Keywords
Field
DocType
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
Rule-based machine translation,Cluster (physics),Artificial intelligence,Cluster analysis,Linguistics,Machine learning,Mathematics,Group decision-making,Information quality
Journal
Volume
Issue
ISSN
23
C
1566-2535
Citations 
PageRank 
References 
38
1.08
26
Authors
5
Name
Order
Citations
PageRank
Bingsheng Liu11778.56
Yinghua Shen21386.12
Yuan Chen3942.72
Xiaohong Chen41384.39
Yumeng Wang5411.49