Abstract
Predicting the completion time of a job is a critical task to a semiconductor fabrication plant. Many recent studies have shown that pre-classifying a job before predicting the completion time was beneficial to prediction accuracy. However, most classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent prediction approach was suitable for the data was questionable. For tackling these problems, a fuzzy-neural approach with back-propagation-network (BPN) post-classification is proposed in this study, in which a job is post-classified with some BPNs instead after predicting its completion time with a fuzzy BPN (FBPN). In this novel way, only jobs which estimated completion times are the same accurate will be clustered into the same category. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate test data. According to experimental results, post-classifying jobs might be very effective in enhancing the accuracy of job completion time prediction in a semiconductor fabrication plant.
This work was support by the National Science Council, R.O.C.
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Chen, T. (2007). A Fuzzy-Neural Approach with BPN Post-classification for Job Completion Time Prediction in a Semiconductor Fabrication Plant. In: Melin, P., Castillo, O., RamÃrez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_59
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DOI: https://doi.org/10.1007/978-3-540-72432-2_59
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