Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering

Authors:
R. J. Kuo;L. M. Lin
Affiliations:
Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section4, Kee-Lung Road, Taipei 106, Taiwan, ROC;AU Optronics Corporation, No. 1, Li-Hsin Road. 2, Hsinchu Science Park, Hsinchu 300, Taiwan, ROC
Venue:
Decision Support Systems
Year:
2010

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Abstract

This study proposes an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Simulational results via Iris, Glass, Vowel and Wine benchmark data sets indicate that the proposed evolutionary-based clustering algorithm is more accurate than the GA-based and PSOA-based clustering algorithms. In addition, the model evaluation results which use order information provided by an international industrial personal computer (PC) manufacturer show that the proposed algorithm is also superior to GA-based and PSOA-based clustering algorithms. Through order clustering, scheduling orders that belong to the same cluster together can reduce production time as well as machine idle time.