A hybrid SOM-BPN approach to lot output time prediction in a wafer fab

Neural Processing Letters

Volumn 24, Issue 3, 2006, Pages 271-288

  Chen, Toly ^a  

^a FENG CHIA UNIVERSITY   (Taiwan)

Author keywords

Back propagation network; Hybrid approach; Output time prediction; Self organization map; Wafer fab

Indexed keywords

BACKPROPAGATION; FUZZY SETS; LEARNING SYSTEMS; SELF ORGANIZING MAPS; TOPOLOGY;

BACK PROPAGATION NETWORK (BPN); CASE BASED REASONING (CBR); HYBRID APPROACH; OUTPUT TIME PREDICTION; PRODUCTION SIMULATION; WAFER FAB;

WSI CIRCUITS;

EID: 33750566327     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-006-9027-4     Document Type: Article

References (19)

1. Barman, S.: The impact of priority rule combinations on lateness and tardiness, IIE Transactions, 30 (1998), 495-504.
2. Chandiramani, V., Jayaseelan, R., Nathan, V. S. L. and Priya, K. S.: A neural network approach to process assignment in multiprocessor systems based on the execution time, In: Proceedings of International Conference on Intelligent Sensing and Information Processing (ICISIP 2004), 332-335, Chennai, India, 2004.
3. Chang, P.-C. and Hsieh, J.-C.: A neural networks approach for due-date assignment in a wafer fabrication factory, International Journal of Industrial Engineering, 10(1) (2003), 55-61.
4. Chang, P.-C, Hsieh, J.-C. and Liao, T. W.: A case-based reasoning approach for due date assignment in a wafer fabrication factory, In: Proceedings of the International Conference on Case-Based Reasoning (ICCBR 2001), Vancouver, British Columbia, Canada, 2001.
5. Chang, P.-C., Hsieh, J.-C. and Liao, T. W.: Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory, Journal of Intelligent Manufacturing, 16 (2005), 549-557.
6. Chen, T.: A fuzzy back propagation network for output time prediction in a wafer fab, Journal of Applied Soft Computing, 2/3F (2003), 211-222.
7. Chiang, J.-H.: A hybrid neural network model in handwritten word recognition, Neural Networks, 11 (1998), 337-346.
8. Foster, W. R., Gollopy, F. and Ungar, L. H.: Neural network forecasting of short, noisy time series, Computers in Chemical Engineering, 16(4) (1992), 293-297.
9. Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.
10. Hung, Y-F. and 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.
11. Jiang, Y and Zhou, Z. H.: SOM ensemble-based image segmentation, Neural Processing Letters, 20 (2004), 171-178.
12. 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.
13. Little, J. D. C.: A proof of the queueing formula L = λW, Operations Research, 9 (1961), 383-387.
14. Piramuthu, S.: Theory and methodology - financial credit-risk evaluation with neural and neuralfuzzy systems, European Journal of Operational Research, 112 (1991), 310-321.
15. Ragatz, G. L. and Mabert, V. A.: A simulation analysis of due date assignment, Journal of Operations Management, 5 (1984), 27-39.
16. Tiwari, M. K. and Roy, D.: Minimization of internal shrinkage in casting using synthesis of neural network, International Journal of Smart Engineering System Design, 4 (2002), 205-214.
17. Vig, M. M. and Dooley, K. J.: Dynamic rules for due-date assignment, International Journal of Production Research, 29(7) (1991), 1361-1377.
18. Wang, L.-X. and Mendel, J. M.: Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man, and Cybernetics, 22(6) (1992), 1414-1427.
19. Weeks, J. K.: A simulation study of predictable due-dates, Management Science, 25 (1979), 363-373.