Authors:
R. J. Kuo
1
;
M. J. Wang
2
;
T. W. Huang
2
and
Tung-Lai Hu
2
Affiliations:
1
National Taiwan University of Science and Technology, Taiwan
;
2
National Taipei University of Technology, Taiwan
Keyword(s):
Clustering analysis, SMT production system, ART2 neural network, Particle swarm optimization algorithm, K-means.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Industrial Applications of Artificial Intelligence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
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.