An Improved Consensus-Based Model for Large Group Decision Making Problems Considering Experts with Linguistic Weighted Information

摘要

In this paper, a consensus-based model in a large group decision making environment is proposed to achieve the group consensus among experts. This framework considers experts with different importance levels, which are expressed by the forms of linguistic variables. More specifically, the group of large-scale experts is firstly transferred into some small groups to simplify the decision making process. Following this, cluster weights can be determined by combining the group size of a cluster and the importance degrees of experts in a cluster. In the consensus process, an objective adjustment coefficient based on the expert's importance degree is developed to modify the individual preferences, which can retain the original decision information of expert with higher importance degree as much as possible. The main characteristic of the proposed objective adjustment coefficient is that generates different coefficients for different clusters based on experts' importance degrees. Finally, the proposed model is applied to an application example, and some detailed analysis and comparisons are given to verify the feasibility and the effectiveness of the proposed method.