A dynamic large-scale multiple attribute group decision-making method with probabilistic linguistic term sets based on trust relationship and opinion correlation

Dynamic large-scale multiple attribute group decision making (DLMAGDM) is ubiquitous in many areas of the real world. It is composed of large numbers of decision makers, several continuous periods, alternative set and attribute set changed with time. Given the charac-teristics implicited in decision-making elements and the advantages of probabilistic lin-guistic term sets (PLTSs) in modelling uncertainty and complexity of decision makers' subjective opinions, this paper constructs a probabilistic linguistic DLMAGDM method. First of all, a dynamic weight determination model based on trust relationships and eviden-tial conflicts between decision makers is proposed to obtain current dynamic weights of decision makers. Then, a comprehensive hierarchical clustering method that divides large numbers of decision makers into several subgroups is constructed based on three cluster-ing constrains. Moreover, some probabilistic linguistic extended evidential power aggrega-tion operators are proposed to aggregate PLTSs. These operators can appropriately handle the extreme PLTSs and fully consider the role of incomplete probabilistic distributions in PLTSs. In addition, a dynamic decision-making method based on PROMETHEE is developed to determine the final priority order of alternatives according to preferences between alter-natives over several periods. Lastly, a case study for supply chain finance risk assessment for several firms in Chinese household appliance industry is utilized to illustrate the prac-ticality and effectiveness of the probabilistic linguistic DLMAGDM method. Furthermore, the comparative analysis with some other existing methods and the sensitivity analysis are made to verify its advantages.(c) 2022 Elsevier Inc. All rights reserved.