A hybrid approach for supplier cluster analysis

To differentiate part suppliers effectively, this study proposed a hybrid approach based on K-means, simulated annealing algorithm (SA), convergence factor particle swarm optimization (CPSO), and the Taguchi method abbreviated as KSACPSO. After all parts suppliers are confirmed by the bill of material (BOM), supplier cluster analysis was conducted on characteristics of customers' demands, including product cost, product quality, and procurement time using the proposed approach. To prove the KSACPSO approach has good clustering performance, the case study of a notebook computer was adopted to carry out the clustering procedures on parts suppliers, and compare the differences between the proposed approach and other hybrid methods. The execution results were analyzed to prove that the efficiency of the suggested KSACPSO approach is superior to K-means, K-means simulated annealing (KSA), K-means genetic algorithm (KGA), K-means genetic simulated annealing (KGSA), and K-means convergence factor particle swarm optimization (KCPSO).

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