Bi-objective Group Decision-Making Based on Hesitant Fuzzy Linguistic Term Sets with Granularity Levels

The existing literature on group decision-making clustering mostly considers single-objective clustering, considering the degree of similarity or consensus within each group. However, both similarity and consensus only reflect the relationship between different evaluation information, and neither takes into account the intrinsic quality of information provided by decision makers, and it is also important to divide them into different groups according to the intrinsic quality of information. In other words, the clustering process of LSGDM should not only consider the similar relationship of information, but also consider the quality of evaluation information, which helps to obtain better and more reasonable decision-making results. Therefore, this chapter explores the hesitant fuzzy linguistic bi-objective group decision-making method from the perspective of granular computing. As we all know, the consensus degree index reflects the similarity of information within a group, and the information entropy index describes the internal quality of information. Therefore, this chapter aims to build a bi-objective group decision-making model with the optimal group consensus degree and group information entropy. Based on the complete G-HFLPRs, an evolutionary algorithm under hesitant fuzzy linguistic environment is developed to solve the bi-objective clustering model, and the preference information in each cluster is integrated to obtain the final result of LSGDM. Finally, the hesitant fuzzy linguistic bi-objective group decision-making method from the perspective of granular computing is applied to the evaluation of personal information protection measures in the late stage of medical emergency.