Multi-criteria group decision-making methods with dynamic probabilistic linguistic information characterized by multiple consecutive time points

With the evolution of complexity in decision-making problems, information is increasingly characterized by dynamic updates. Therefore, we introduce the concept of dynamic probabilistic linguistic term set (DPLTS) to solve the multi-criteria group decision-making (MCGDM) problems with dynamic probabilistic linguistic information characterized by multiple consecutive time points. DPLTS can help experts provide theoretical basis for dynamic decision-making problems. By combining real-time data with analytical models, it can provide decision-makers with effective strategies. Then we present some basic operations and aggregation operators for DPLTSs. The belief interval (BI) interpretation based on Dempster-Shafer evidence theory and BI measure of DPLTSs are also presented to reduce information uncertainty. After that, we construct two MCGDM methods, including the aggregation-based method and the BI method. We use the concept of time degree to reflect the cognition of the importance of multiple consecutive time points for decision-makers and obtain the time weight vector with the help of a nonlinear programming model. Finally, the proposed methods are applied to a case of pharmaceutical innovation capacity evaluation. By the sensitivity analysis of time degree, the characteristics of the methods are summarized and compared, then the effectiveness of the methods is proved.