Abstract
Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly complicated and involve more than hundred of production steps. Therefore, a simulation model with the production process of a real wafer fab located in Hsin-Chu Science-based Park of Taiwan is built. In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is proposed. And Genetic Algorithm (GA) is applied to fine-tune the weights of features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, corresponding GA-CBR is selected and applied for flow time prediction. Finally, using the simulated data, the effectiveness of the proposed method (SGA-CBR) is shown by comparing with other approaches.
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References
Brill, F.Z., Brown, D.E., Martin, W.N.: Fast Genetic Selection of Features for Neural Network Classifiers. IEEE Transactions on Neural Networks 32, 324–328 (1992)
Chang, P.C., Hsieh, J.C.: A Neural Networks Approach for Due Date Assignment in A Wafer Fabrication Factory. Int. J. Ind. Eng. 10, 55–61 (2003)
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(5), 549–557 (2005)
Chang, P.C., Liao, T.W.: Combing SOM and Fuzzy Rule Base for Flow Time Prediction in Semiconductor Manufacturing Factory. Applied Soft Computing 6, 198–206 (2006)
Chiu, C.C.: A Case-based Customer Classification Approach for Direct Marketing. Expert Systems with Applications 22, 163–168 (2002)
Chiu, C.C., Chang, P.C., Chiu, N.H.: A Case-based Expert Support System for Due Date Assignment in A Wafer Fabrication Factory. J. Intell. Manuf. 14, 287–296 (2003)
Finnie, G.R., Witting, G.E.: Estimating Software Development Effort with Case-Based Reasoning. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266, pp. 13–22. Springer, Heidelberg (1997)
Jo, H., Han, I., Lee, H.: Bankruptcy Prediction Using Case-Based Reasoning, Neural Networks and Discriminant Analysis. Expert Systems and Applications 13, 97–108 (1997)
Kim, K., Han, I.: Maintaining Case-based Reasoning Systems Using A Genetic Algorithms Approach. Expert Systems with Applications 21, 139–145 (2001)
Kim, S.H., Shin, S.W.: Identifying the Impact of Decision Variables for Nonlinear Classification Tasks. Expert Systems with Applications 18, 201–214 (2000)
Kolodner, J.L.: An Introduction to Case-Based Reasoning. Artificial Intelligence Review 6, 3–34 (1992)
Liao, T.W., Zhang, Z.M., Mount, C.R.: A Case-Based Reasoning System for Identifying Failure Mechanisms. Engineering Applications of Artificial Intelligence 13, 199–213 (2000)
Louis, S.J., Xu, Z.: Genetic Algorithms for Open Shop Scheduling and Re-Scheduling. In: Proceedings of the ISCA 11th International Conference on Computers and Their Applications, pp. 99–102 (1996)
Ramsey, C., Grefensttete, J.: Case-based Initialization of Genetic Algorithms. In: Proceeding of the Fifth International Conference on Genetic Algorithms, San Mateo, California (1993)
Shin, K., Han, I.: Case-Based Reasoning Supported by Genetic Algorithms for Corporate Bond Rating. Expert Systems with Applications 16, 85–95 (1999)
Siedlecki, W., Sklansky, J.: A Note on Genetic Algorithms for Large-Scale Feature Selection. Pattern Recognition Letters 10, 335–347 (1989)
Watson, I., Gardingen, D.: A Distributed Cased-Based Reasoning Application for Engineering Sales Support. In: Proc. 16th Int. Joint Conf. on Artificial Intelligence, vol. 1, pp. 600–605 (1999)
Watson, I., Watson, H.: CAIRN: A Case-Based Document Retrieval System. In: Filer, N., Watson, I. (eds.) Proc. of the 3rd United Kingdom Case-Based Reasoning Wrokshop, University of Manchester (1997)
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Chang, PC., Wang, YW., Liu, CH. (2006). Combining SOM and GA-CBR for Flow Time Prediction in Semiconductor Manufacturing Factory. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_79
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DOI: https://doi.org/10.1007/11908029_79
Publisher Name: Springer, Berlin, Heidelberg
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