| Summary: | 碩士 === 國立臺北科技大學 === 工業工程與管理系所 === 94 === This study intends to propose a novel clustering analysis technique, particle swarm K-means optimization (PSKO), which integrates both the particle swarm optimization and K-means method. In order to evaluate its computational performance, three clustering analysis methods including K-means, PSO clustering and Hybrid PSO method are employed for comparison via IRIS, Glass, Vowel and Image Segmentation benchmark data sets. The simulation results indicate that PSKO outperforms these three methods both in speed and accuracy.
For further assessing PSKO’s capability, a world-class industrial computer manufacturer, Advantech company, which belongs to the high mix low volume production system, provides the related evaluation information. Its production characteristic is that the material preparation process often has not completed during setup in SMT system. This results in expensive machine idleness. Thus, we apply a two-stage method, which first uses the adaptive resonance theory 2(ART2) network to determine the number of clusters and then employs K-Means, PSO clustering, Hybrid PSO, PSKO algorithms to find the final solution. The results show that the best two-stage method is ART2+PSKO. Through order clustering, the production planners can manufacture products in the same cluster in order to save the material preparation time and also achieve reducing SMT setup time.
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