A hesitant fuzzy linguistic bi-objective clustering method for large-scale group decision-making. (15th April 2021)

• Record Type: Journal Article
• Authors: Zheng, Yuanhang; Xu, Zeshui; He, Yue; Tian, Yuhang
• Is Part Of: Expert systems with applications. Volume 168(2021)
• Publication Date: 2021-04-15
• Subjects: Large-scale group decision-making -- Bi-objective clustering -- Evolutionary algorithm -- Hesitant fuzzy linguistic preference relation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114355
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Large-scale group decision-making process has received an increasing attention in recent years. After making the general survey of the existing large-scale group decision-making methods, we have found that: 1) consistency threshold value of hesitant fuzzy linguistic preference relation is fixed in traditional consistency measures; 2) the clustering process of LSGDM does not consider the similar relationship between different evaluation information and the information quality simultaneously. Thus, in order to tackle the above issues and describe the hesitancy of experts in the decision-making process, the paper proposes a hesitant fuzzy linguistic bi-objective clustering method considering consensus and information entropy for tackling large-scale group decision-making problems. Firstly, a selection procedure for preference information is developed to quickly select suitable experts who meet the consistency requirements. Then, a bi-objective clustering method based on the group consensus degree indicator and group information entropy indicator is proposed to divide the experts into different clusters, considering the similar relationship and the quality of evaluation information simultaneously. After that, comprehensive preference information and the overall ranking of alternatives can be obtained. In the end, an illustrative example of choosing the optimal way to protect the personal information while defending against COVID-19 and some comparative study show that the proposed method is valid for large-scale group decision-making problems and has good performance and strong robustness. Highlights: Provide a new perspective for large-scale group decision-making Conduct a new way to check the consistency of linguistic preference relation Develop a hesitant fuzzy linguistic bi-objective clustering method Apply the proposed method to evaluate schemes of protecting personal information