A non-threshold consensus model based on the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making

This study proposes a non-threshold consensus model that combines the minimum cost and maximum consensus increasing for multi-attribute large group decision-making (MALGDM). First, the large-scale experts is classified into several clusters via the combination of the similarities of evaluation information, unit consensus cost, and adjustment willingness. Then, a more sensitive consensus measure method that combines the mean value and variance of the similarities among clusters is presented. Next, a comprehensive identification rule is put forward to determine the cluster with a low consensus level, low unit consensus cost, and high adjustment willingness for information adjustment. An optimization model that combines the minimization of the cost of the cluster and the maximization of the increase of the global consensus level is then constructed to obtain the adjusted information. Also, the adjustment willingness is considered in the constraints to limit the adjustment range. Moreover, instead of the use of a predefined threshold and a maximum number of iterations, a termination index is developed to terminate the consensus reaching process (CRP) to make the CRP more objective and rational. Finally, an application example is presented, and comparison and simulation analyses are performed to validate the feasibility and effectiveness of the proposed model.

Automated summary

This study introduces a non-threshold consensus model for multi-attribute large group decision-making (MALGDM), which classifies experts into clusters, determines adjustment clusters using a sensitive consensus measure method, and constructs an optimization model to obtain adjusted information. The model aims to improve global consensus levels efficiently and effectively.