AN ORDER CLUSTERING SYSTEM USING ART2 NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION METHODN
R. J. Kuo, M. J. Wang, T. W. Huang, Tung-Lai Hu
2009
Abstract
Surface mount technology (SMT) production system set up is quite time consuming for industrial personal computers (PC) because of high level of customization. Therefore, this study intends to propose a novel two-stage clustering algorithm for grouping the orders together before scheduling in order to reduce the SMT setup time. The first stage first uses the adaptive resonance theory 2 (ART2) neural network for finding the number of clusters and then feed the results to the second stage, which uses particle swarm K-means optimization (PSKO) algorithm. An internationally well-known industrial PC manufacturer provided the related evaluation information. The results show that the proposed clustering method outperforms other three clustering algorithms. Through order clustering, scheduling products belonging to the same cluster together can reduce the production time and the machine idle time.
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Paper Citation
in Harvard Style
Kuo R., Wang M., Huang T. and Hu T. (2009). AN ORDER CLUSTERING SYSTEM USING ART2 NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION METHODN . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 55-60. DOI: 10.5220/0001860300550060
in Bibtex Style
@conference{iceis09,
author={R. J. Kuo and M. J. Wang and T. W. Huang and Tung-Lai Hu},
title={AN ORDER CLUSTERING SYSTEM USING ART2 NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION METHODN},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={55-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001860300550060},
isbn={978-989-8111-85-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AN ORDER CLUSTERING SYSTEM USING ART2 NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION METHODN
SN - 978-989-8111-85-2
AU - Kuo R.
AU - Wang M.
AU - Huang T.
AU - Hu T.
PY - 2009
SP - 55
EP - 60
DO - 10.5220/0001860300550060