Abstract
In group decision-making problems, experts often form multiple subgroups depending upon their backgrounds or attitudes. To explore the effect of different types of subgroups' formation on the final decision and to make the decision process more conforming to human custom, we propose a comprehensive study on the effect of "multi-subgroup background" on a basis of weights optimization and Granular Computing techniques. Firstly, a comparison of different types of clustering strategies is realized to find the most suitable method for clustering experts. Since Analytical Hierarchy Process is selected as the fundamental model, it becomes a reciprocal matrices' or eigenvectors' clustering task. Secondly, allocation strategies of information granularity to each expert along with weight to each subgroup are carefully designed and optimized. Information granularity is viewed as a design asset to provide experts with some flexibility to adjust their evaluation results, whereas weights reflect the importance degrees of different subgroups and further help to increase the consensus. A granulation-degranulation process is developed to evaluate the performance of a set of weights and granularities under a multi-criteria objective's guidance. Thirdly, a proper evolutionary optimization method like particle swarm optimization is redesigned to iteratively generate the best set of information granularities and weights under an equality constraint condition. A series of numerical studies on synthetic data and real-world data are executed to verify the effectiveness of our method. The results show same trend present across experts.