Dynamic Expert Reliability Based Feedback Mechanism in Consensus Reaching Process with Distributed Preference Relations

A group consensus reaching process (CRP) based on dynamic expert reliability is proposed in this paper. The method is designed to support uncertain multi-attribute group decision making in situations where experts in a group use distributed preference relations (DPRs) to express their preferences when making a decision. In the method, it is assumed that a predefined consensus requirement can be specified and must be satisfied before consensus-based solutions are generated. Consensus measures of DPRs are constructed to ensure consensus convergence and used to check whether the predefined consensus requirement at a specific level is satisfied. If the requirement is not satisfied, expert reliability is first defined and calculated in terms of data depicted by the experts, and then used to design an expert reliability based feedback mechanism composed of identification and suggestion rules to help identify the DPRs hindering CRP. Additionally, experts update their DPRs to accelerate convergence to CRP. Arguably, it is the first attempt to introduce expert reliability in consensus convergence. Once the predefined consensus requirement is satisfied, experts’ preferences are aggregated to generate a consensus-based solution. The problem of selecting an appropriate supplier in a high-end equipment manufacturing enterprise located in Changzhou, Jiangsu Province, China is analyzed by the proposed method to demonstrate its applicability and validity.