Measuring and reaching consensus in group decision making with the linguistic computing model based on discrete fuzzy numbers

• Published in: Applied soft computing Vol. 77; pp. 135 - 154
• Main Authors: Ma, Xiao-yu, Zhao, Meng, Zou, Xiao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2019
• Subjects: Aggregation; Discrete fuzzy numbers; Group consensus; Group decision making; Linguistic model; Aggregation; Group decision making; Group consensus; Linguistic model; Discrete fuzzy numbers
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.01.008

The linguistic computing model based on discrete fuzzy numbers has some good properties compared with other existing models and should be further studied, which has been proved by some researchers. However, the research of group consensus with this linguistic model is insufficient, given that group consensus is an important issue in group decision making. Therefore, this paper would concentrate on this subject. It includes two main issues: research on consensus measure and research on the method for improving group consensus in group decision making based on this linguistic computing model. For research on the consensus measure, this paper first studies on the aggregation method for discrete fuzzy numbers. Then, the index of measuring group consensus is determined. For research on improving the group consensus, considering the characteristics of discrete fuzzy numbers, we present an algorithm to improve group consensus. In addition, an illustrative example of a decision-making problem about investment is stated to show the whole solving process. It also illustrates the feasibility, rationality and validity of all the proposed methods. Finally, the comparisons between some proposals and existing studies are made, which helps point out the advantages of the proposed methods. •The linguistic model based on discrete fuzzy numbers (LM-DFN) is studied.•The method for measuring group consensus with the LM-DFN is proposed.•The method for improving group consensus with the LM-DFN is proposed.•The proposed methods have some advantages compared with the existing ones.