Heterogeneous Large-Scale Group Decision Making Using Fuzzy Cluster Analysis and Its Application to Emergency Response Plan Selection

As the number of people involved in a decision-making problem increases, complexity group (GDM) process increases accordingly. The size participants and heterogeneous information have important effects on consensus reaching GDM. To deal with these two issues, traditional methods divide large groups into smaller ones to reduce scale GDM translate uniform format handle heterogeneity problem. These face challenges: 1) how determine appropriate size? 2) avoid or loss during transformation process? address challenges, this article uses fuzzy cluster analysis integrate for large-scale problems. First, is divided using F-statistic applied satisfactory clusters. original retained based similarity degree. Then, conducted within small form unified opinion. A feedback mechanism developed adjust matrix when any cannot reach consensus, technique order preference by an ideal solution (TOPSIS) used select best alternative. validate proposed approach, experiment study practical example selecting rescue plan emergency situation. result shows that approach helps choose faster.