Probabilistic Linguistic Multiple Attribute Group Decision-Making Based on a Choquet Operator and Its Application in Supplier Selection

As an enhanced version of traditional linguistic term sets, Probabilistic Linguistic Term Sets (PLTS) incorporate probabilistic information, thereby offering a more robust approach to Multiple Attribute Group Decision-Making (MAGDM) and significantly improving its efficacy. This paper proposes two novel information aggregation operators for PLTS to address MAGDM problems in the PLTS context. Firstly, we introduce Choquet integral-based generalized arithmetic and geometric operators, which are designed to fuse decision information expressed by different PLTSs, thereby more comprehensively considering the interrelationships among various attributes. Subsequently, we further define measures of group consistency and inconsistency for individual decision information in MAGDM, which are used to determine the information weights of decision-makers. Finally, the group decision information is aggregated using the proposed PLTS aggregation operators. The effectiveness as well as the applicability of the developed method are illustrated through numerical examples and comparative analysis.