Abstract
In social network group decision-making (SNGDM) problems, decision-makers (DMs) often express their opinions or preferences using probabilistic linguistic term sets (PLTSs). In this paper, a novel SNGDM method for probabilistic linguistic information is proposed. Firstly, to obtain the prioritization of DMs in the clustering process, a DM clustering method for SNGDM is developed considering the influence of trust relationships and opinions similarity among DMs. Then, to satisfy the requirements of the consensus reaching process in SNGDM, a dynamic consensus threshold calculation method based on an optimization model is introduced. Furthermore, in the consensus measure stage, a novel consensus measure method for both DMs and subgroups is proposed, using a stochastic multi-criteria acceptability analysis (SMAA) method. Based on the consensus measure method, a novel SNGDM method based on SMAA for PLTSs is proposed. Finally, a case study of service quality evaluation in institutional pensions is used to illustrate the effectiveness of the proposed method. The results of case show that the proposed method can adjust consensus thresholds dynamically based on each round of collective opinions, and can solve the SNGDM problems when DMs can not provide their preferences for criteria weights.
Published Version
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