Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information

Unlike other linguistic modelings, probabilistic linguistic terms can clearly describe the importance of different linguistic terms. With respect to group decision-making (GDM) problems, it is convenient for experts to express their evaluation opinions with probabilistic linguistic preference relations (PLPRs), which can transform experts' quantitative descriptions into qualitative probabilistic linguistic terms. The processes of consistency-adjustment and expert weights determination play a key role in GDM. Therefore, this paper aims at the design of a novel probabilistic linguistic GDM method with consistency-adjustment algorithm and trust relationship-driven expert weight determination model. First, we redefine the multiplicative consistency of PLPRs, which only involves changing the probabilities of linguistic terms. A new distance between PLPRs is presented to calculate the consistency index. Then, we propose a convergent consistency-adjustment algorithm to improve the consistency of a PLPR to an acceptable consistency level. Subsequently, a trust relationship-driven expert weight determination model is developed to derive the experts' weights in a social network environment. Finally, a probabilistic linguistic GDM method is designed to determine the reliable ranking of alternatives. The advantages and applicability of the proposed method are illustrated by a case study concerning an evaluation of logistics service suppliers and associated comparative analyses. (C) 2021 Elsevier B.V. All rights reserved.