Integration of growing self-organizing map and bee colony optimization algorithm for part clustering

This study proposes a two-stage clustering algorithm for part clustering based on a self-organizing map (SOM) algorithm. The SOM algorithm has been applied widely in clustering since it can represent data pattern in two-dimensional topology. However, SOM algorithm requires a predetermined topology shape and connecting weights within the topology. Both of them can seriously influence the clustering performance. The proposed algorithm involves growing self-organizing map (GSOM) algorithm and bee colony optimization based self-organizing map (BCOSOM) to determine the SOM topology and improve the performance of SOM. In the first stage, GSOM is applied to determine the SOM topology. It is then followed by BCOSOM to fine-tune the SOM weights. The proposed GSOM + BCOSOM algorithm is compared with other algorithms, namely, particle swarm optimization (PSO), BCO, SOM, GSOM + PSOSOM, SOM + PSO, and SOM + BCO algorithms, using seven benchmark data sets. The computational results indicate that GSOM + BCOSOM algorithm is able to find a better solution than other algorithms. Furthermore, the proposed algorithm is also applied to a real-world problem about clustering components into part families for a medical furniture manufacturer in Indonesia. By clustering the components, the manufacturer can reduce production lead-time, setup time, production time, and work-in-process inventory in order to enhance the manufacturing efficiency. According to the GSOM + BCOSOM result, the components are grouped into three clusters. This study also identifies the corresponding characteristics of each cluster.