Title: An evolving hybrid neural approach for predicting job completion time in a semiconductor fabrication plant
Authors: Toly Chen, Yi-Chi Wang
Addresses: Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City 407, ROC. ' Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City 407, ROC
Abstract: Predicting job completion time is an important but difficult task to a semiconductor fabrication plant. To further enhance its effectiveness, an evolving hybrid neural approach is proposed in this study. To evaluate the effectiveness of the proposed approach, Production Simulation (PS) is also employed to generate test data. According to experimental results, the predicting accuracy of the evolving hybrid neural approach is significantly better than those of many existing approaches. In addition, to improve the practicability of the evolving hybrid neural approach, several issues in practical applications are addressed and discussed. Though the proposed evolving hybrid neural approach seems to be theoretically complicated, its ease of implementation on the production planning and control for a semiconductor plant is demonstrated in this study. [Received 10 October 2008; Revised 05 March 2009; Revised 13 June 2009; Accepted 14 June 2009]
Keywords: job completion times; semiconductor fabrication; look-ahead; backpropagation networks; neural networks; BPN; self-organising maps; SOM; genetic algorithms; simulation; production planning.
European Journal of Industrial Engineering, 2010 Vol.4 No.3, pp.336 - 354
Published online: 01 Jun 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article