A Consensus Model of Probabilistic Linguistic Preference Relations in Group Decision Making Based on Feedback Mechanism

Abstract

Owing to the important role in group decision making (GDM), the consensus research has received extensive attention from both theoretical and applied perspectives in recent years. This paper focuses on the emerging decision-making tool, namely probabilistic linguistic preference term sets (PLTSs) in GDM. A novel approach is proposed to compute the consensus level between any two probabilistic linguistic preference relations (PLTRs) by defining the dominance degree and similarity degree. Then, a two-stage consensus model is constructed to assist decision makers in achieving a high consensus level by adjusting and improving the PLTRs. Finally, illustrative examples demonstrate the usefulness of the presented consensus model. The main contribution of this paper is twofold: One is a novel approach to measure the consensus level in GDM with PLTRs. Another is to propose a consensus improvement method with the feedback mechanism.