Bi-objective Optimization Method for Large-Scale Group Decision Making based on Hesitant Fuzzy Linguistic Preference Relations with Granularity Levels

Large-scale group decision making becomes increasingly common with the rapid development of society and the increasing complexity of practical problems. However, it is difficult to distinguish the semantic differences between the same linguistic term, and original linguistic term may not express flexible semantics, so that this will affect the precise of decision-making results. With the help of granular computing, this paper adopts a new format of linguistic term, named as hesitant fuzzy linguistic term set with granularity level, to endow preference information with flexibility and at a specific granularity. Then, in this study, we propose a novel intelligent bi-objective optimization method for large-scale group decision making, considering group consensus degree and group risk degree in the decision-making process, where group risk degree is measured from the motivation of portfolio risk. Differential evolution is used to handle bi-objective optimization method to determine the optimal results. We also introduce an additive consistency measure and develop a method to derive the corresponding threshold values through Monte Carlo simulation. Finally, the case study and comparison results are covered to demonstrate the practicality and superiority of the proposed method. This work has some original points: 1) Hesitant fuzzy linguistic term set with granularity level brings flexibility to the decision-making process. 2) Group consensus degree and group risk degree are involved in bi-objective optimization method, where the group risk degree is measured from the motivation of portfolio risk. 3) A novel additive consistency measure is proposed and different threshold values of preference relations in different dimensions are derived.