A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection

In multi-attribute group decision making, it is preferable that a set of experts reach a high degree of consensus amongst their opinions, especially for large-scale group decision making. This paper presents a two-stage method to support the consensus reaching process for large-scale multi-attribute group decision making problems. The first stage classifies the large-scale group into several sub-clusters by utilizing the self-organizing maps and, then, an iterative algorithm is proposed to obtain the group preference for each sub-cluster. The second stage treats the group preference of each sub-cluster as the representative preference and collapses each sub-cluster to form a smaller and more manageable group. Then the aforesaid iterative algorithm is utilized to process the new set and select the best alternative(s). Finally, a case study of real application to earthquake shelter selection and comparative analysis are given to verify the effectiveness of the proposed method.