An Automatic Consensus Reaching Approach With Preference Adjustment Willingness for Group Decision-Making

Most of the existing consensus reaching approaches for group decision-making (GDM) need decisionmakers to reevaluate alternatives multiple times or require additional external interventions, which results in great time and labor consumption. To this end, automatic consensus reaching approaches have been developed to improve effectiveness and efficiency. However, in the existing automatic consensus reaching approaches, preference adjustment willingness (PAW) is overlooked and consistency/consensus thresholds cannot be guaranteed to be reached, which may lead to an unacceptable collective decision. This study proposes a new automatic consensus reaching approach with PAW for GDM. First, under the paradigm of interval information granularity, PAW is formally defined by considering both the amount and sensitivity of preference adjustment. Levels of interval information granularity are adaptively determined by an algorithm with achievement of the consistency threshold. Then, we propose an automatic preference adjustment model where the weighted average of PAW, group consensus, and individual consistency is defined as its performance index. Based on the proposed model and algorithm, a consensus evolution realizing a continuous improvement of consensus is subsequently developed. Finally, the framework of the proposed approach is presented. The applicability of the proposed approach was analyzed through a case study concerning the concept selection of a turbofan engine's component. Discussions on parameter settings and time-consuming of the approach show its great effectiveness and time efficiency. Comparison with other similar approaches demonstrates that the proposed approach is able to achieve larger consistency and consensus improvement with a smaller preference adjustment amount.