Abstract
Predicting the completion time of a job is a critical task to a wafer fabrication plant (wafer fab). 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 recurrent hybrid neural network is proposed in this study, in which a job is pre-classified into one category with the k-means (kM) classifier, and then the back propagation network (BPN) tailored to the category is applied to predict the completion time of the job. After that, the prediction error is fed back to the kM classifier to adjust the classification result, and then the completion time of the job is predicted again. After some replications, the prediction accuracy of the hybrid kM-BPN system will be significantly improved.
This work was support by the National Science Council, R.O.C.
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References
Barman, S.: The Impact of Priority Rule Combinations on Lateness and Tardiness. IIE Transactions 30, 495–504 (1998)
Chang, P.-C., Hsieh, J.-C.: A Neural Networks Approach for Due-date Assignment in a Wafer Fabrication Factory. International Journal of Industrial Engineering 10(1), 55–61 (2003)
Chang, P.-C., Hsieh, J.-C., Liao, T.W.: A Case-based Reasoning Approach for Due Date Assignment in a Wafer Fabrication Factory. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, Springer, Heidelberg (2001)
Chang, P.-C., Hsieh, J.-C., Liao, T.W.: Evolving Fuzzy Rules for Due-date Assignment Problem in Semiconductor Manufacturing Factory. Journal of Intelligent Manufacturing 16, 549–557 (2005)
Chen, T.: A Fuzzy Back Propagation Network for Output Time Prediction in a Wafer Fab. Applied Soft Computing 2/3F, 211-222 (2003)
Chen, T.: A Fuzzy Set Approach for Evaluating the Achievability of an Output Time Forecast in a Wafer Fabrication Plant. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 483–493. Springer, Heidelberg (2006)
Chen, T.: A Hybrid Look-ahead SOM-FBPN and FIR System for Wafer-lot-output Time Prediction and Achievability Evaluation. International Journal of Advanced Manufacturing Technology (2007), doi:10.1007/s00170-006-0741-x
Chen, T.: A Hybrid SOM-BPN Approach to Lot Output Time Prediction in a Wafer Fab. Neural Processing Letters 24(3), 271–288 (2006)
Chen, T.: A Look-ahead Fuzzy Back Propagation Network for Lot Output Time Series Prediction in a Wafer Fab. In: King, I., et al. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 974–982. Springer, Heidelberg (2006)
Chen, T.: Applying an Intelligent Neural System to Predicting Lot Output Time in a Semiconductor Fabrication Factory. In: King, I., et al. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 581–588. Springer, Heidelberg (2006)
Chen, T., Jeang, A., Wang, Y.-C.: A Hybrid Neural Network and Selective Allowance Approach for Internal Due Date Assignment in a Wafer Fabrication Plant. International Journal of Advanced Manufacturing Technology (2007), doi:10.1007/s00170-006-0869-8
Chen, T., Lin, Y.-C.: A Hybrid and Intelligent System for Predicting Lot Output Time in a Semiconductor Fabrication Factory. In: Greco, S., et al. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 757–766. Springer, Heidelberg (2006)
Chung, S.-H., Yang, M.-H., Cheng, C.-M.: The Design of Due Date Assignment Model and the Determination of Flow Time Control Parameters for the Wafer Fabrication Factories. IEEE Transactions on Components, Packaging, and Manufacturing Technology – Part C 20(4), 278–287 (1997)
Foster, W.R., Gollopy, F., Ungar, L.H.: Neural Network Forecasting of Short, Noisy Time Series. Computers in Chemical Engineering 16(4), 293–297 (1992)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Hung, Y.-F., Chang, C.-B.: Dispatching Rules Using Flow Time Predictions for Semiconductor Wafer Fabrications. In: Proceedings of the 5th Annual International Conference on Industrial Engineering Theory, Applications and Practice, Taiwan (2001)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed Representation of Fuzzy Rules and Its Application to Pattern Classification. Fuzzy Sets and Systems 52(1), 21–32 (1992)
Lin, C.-Y.: Shop Floor Scheduling of Semiconductor Wafer Fabrication Using Real-time Feedback Control and Prediction. Ph.D. Dissertation, Engineering-Industrial Engineering and Operations Research, University of California at Berkeley (1996)
Piramuthu, S.: Theory and Methodology – Financial Credit-risk Evaluation with Neural and Neuralfuzzy Systems. European Journal of Operational Research 112, 310–321 (1991)
Ragatz, G.L., Mabert, V.A.: A Simulation Analysis of Due Date Assignment. Journal of Operations Management 5, 27–39 (1984)
Vig, M.M., Dooley, K.J.: Dynamic Rules for Due-date Assignment. International Journal of Production Research 29(7), 1361–1377 (1991)
Wang, L.-X., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man, and Cybernetics 22(6), 1414–1427 (1992)
Weeks, J.K.: A Simulation Study of Predictable Due-dates. Management Science 25, 363–373 (1979)
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Chen, T. (2007). Predicting Job Completion Time in a Wafer Fab with a Recurrent Hybrid Neural Network. 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_23
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DOI: https://doi.org/10.1007/978-3-540-72432-2_23
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