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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 956978, 14 pages
http://dx.doi.org/10.1155/2013/956978
Research Article

A Fuzzy-Neural Ensemble and Geometric Rule Fusion Approach for Scheduling a Wafer Fabrication Factory

Hsin-Chieh Wu1 and Toly Chen2

1Department of Industrial Engineering and Management, Chaoyang University of Science and Technology, Taiwan
2Department of Industrial Engineering and Systems Management, Feng Chia University, Taiwan

Received 22 March 2013; Accepted 13 June 2013

Academic Editor: Pedro Ponce

Copyright © 2013 Hsin-Chieh Wu and Toly Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. N. Wang and C. H. Wang, “A simulated model for cycle time reduction by acquiring optimal lot size in semiconductor manufacturing,” International Journal of Advanced Manufacturing Technology, vol. 34, no. 9-10, pp. 1008–1015, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Chen and Y. C. Lin, “A fuzzy-neural fluctuation smoothing rule for scheduling jobs with various priorities in a miconductor manufacturing factory,” International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 17, no. 3, pp. 397–417, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Chen and Y. C. Wang, “A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory—a simulation study,” International Journal of Advanced Manufacturing Technology, vol. 45, no. 1-2, pp. 110–121, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. D. A. Koonce and S. C. Tsai, “Using data mining to find patterns in genetic algorithm solutions to a job shop schedule,” Computers and Industrial Engineering, vol. 38, no. 3, pp. 361–374, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. B. W. Hsieh, C. H. Chen, and S. C. Chang, “Scheduling semiconductor wafer fabrication by using ordinal optimization-based simulation,” IEEE Transactions on Robotics and Automation, vol. 17, no. 5, pp. 599–608, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. H. J. Yoon and W. Shen, “A multiagent-based decision-making system for semiconductor wafer fabrication with hard temporal constraints,” IEEE Transactions on Semiconductor Manufacturing, vol. 21, no. 1, pp. 83–91, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Harrath, B. Chebel-Morello, and N. Zerhouni, “A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 7, pp. 280–285, October 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Sourirajan and R. Uzsoy, “Hybrid decomposition heuristics for solving large-scale scheduling problems in semiconductor wafer fabrication,” Journal of Scheduling, vol. 10, no. 1, pp. 41–65, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. A. K. Gupta and A. I. Sivakumar, “Job shop scheduling techniques in semiconductor manufacturing,” International Journal of Advanced Manufacturing Technology, vol. 27, no. 11-12, pp. 1163–1169, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Altendorfer, B. Kabelka, and W. Stöcher, “A new dispatching rule for optimizing machine utilization at a semiconductor test field,” in Proceedings of the IEEE/SEMI Advanced Semiconductor Manufacturing Conference (ASMC '07), pp. 188–193, Stresa, Italy, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Zhang, Z. Jiang, and C. Guo, “Simulation-based optimization of dispatching rules for semiconductor wafer fabrication system scheduling by the response surface methodology,” International Journal of Advanced Manufacturing Technology, vol. 41, no. 1-2, pp. 110–121, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Cao, Y. Peng, and Y. Wang, “A drum-buffer-rope based scheduling method for semiconductor manufacturing system,” in Proceedings of the 7th IEEE International Conference on Automation Science and Engineering (CASE '11), pp. 120–125, Trieste, Italy, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. H. Lee, C. T. Chang, D. S. H. Wong, and S. S. Jang, “Petri-net based scheduling strategy for semiconductor manufacturing processes,” Chemical Engineering Research and Design, vol. 89, no. 3, pp. 291–300, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. I. L. Wang, Y. C. Wang, and C. W. Chen, “Scheduling unrelated parallel machines in semiconductor manufacturing by problem reduction and local search heuristics,” Flexible Services and Manufacturing Journal, vol. 25, no. 3, pp. 343–366, 2013. View at Publisher · View at Google Scholar
  15. T. Chen, “Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: a simulation study,” Proceedings of the Institution of Mechanical Engineers B, vol. 223, no. 8, pp. 1033–1043, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Hu, L. Zhen, Z. Sun, and H. Zhang, “A multi-stage fluctuation smoothing method for multiple bottlenecks in wafer fabrication,” International Journal of Advanced Manufacturing Technology, 2013. View at Publisher · View at Google Scholar
  17. C. C. Chern and Y. L. Liu, “Family-based scheduling rules of a sequence-dependent wafer fabrication system,” IEEE Transactions on Semiconductor Manufacturing, vol. 16, no. 1, pp. 15–24, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. T. Chen, “A tailored non-linear fluctuation smoothing rule for semiconductor manufacturing factory scheduling,” Proceedings of the Institution of Mechanical Engineers I, vol. 223, no. 2, pp. 149–160, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Chen, “Intelligent scheduling approaches for a wafer fabrication factory,” Journal of Intelligent Manufacturing, vol. 23, no. 3, pp. 897–911, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. R. M. Dabbas and J. W. Fowler, “A new scheduling approach using combined dispatching criteria in wafer fabs,” IEEE Transactions on Semiconductor Manufacturing, vol. 16, no. 3, pp. 501–510, 2003. View at Publisher · View at Google Scholar · View at Scopus
  21. R. M. Dabbas, H. N. Chen, J. W. Fowler, and D. Shunk, “A combined dispatching criteria approach to scheduling semiconductor manufacturing systems,” Computers and Industrial Engineering, vol. 39, no. 3-4, pp. 307–324, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. T. C. Chiang, Y. S. Shen, and L. C. Fu, “A new paradigm for rule-based scheduling in the wafer probe centre,” International Journal of Production Research, vol. 46, no. 15, pp. 4111–4133, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Chen and Y. C. Wang, “A bi-criteria nonlinear fluctuation smoothing rule incorporating the SOM-FBPN remaining cycle time estimator for scheduling a wafer fab-a simulation study,” International Journal of Advanced Manufacturing Technology, vol. 49, no. 5-8, pp. 709–721, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Chen, Y. C. Wang, and Y. C. Lin, “A bi-criteria four-factor fluctuation smoothing rule for scheduling jobs in a wafer fabrication factory,” International Journal of Innovative Computing, Information and Control, vol. 6, no. 10, pp. 4289–4303, 2010. View at Google Scholar · View at Scopus
  25. Y. D. Kim, D. H. Lee, J. U. Kim, and H. K. Roh, “A simulation study on lot release control, mask scheduling, and batch scheduling in semiconductor wafer fabrication facilities,” Journal of Manufacturing Systems, vol. 17, no. 2, pp. 107–117, 1998. View at Google Scholar · View at Scopus
  26. R. M. Dabbas, J. W. Fowler, D. A. Rollier, and D. McCarville, “Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling,” International Journal of Production Research, vol. 41, no. 5, pp. 939–961, 2003. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Li and O. Sigurdur, “Data mining for best practices in scheduling data,” in Proceedings of the IIE Annual Conference and Exhibition, p. 853, May 2004. View at Scopus
  28. T. Chen, “An optimized tailored nonlinear fluctuation smoothing rule for scheduling a semiconductor manufacturing factory,” Computers and Industrial Engineering, vol. 58, no. 2, pp. 317–325, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. T. Chen, “A nonlinear programming and artificial neural network approach for optimizing the performance of a job dispatching rule in a wafer fabrication factory,” Applied Computational Intelligence and Soft Computing, vol. 2012, Article ID 471973, 9 pages, 2012. View at Publisher · View at Google Scholar
  30. T. Chen, “Incorporating fuzzy c-means and a back-propagation network ensemble to job completion time prediction in a semiconductor fabrication factory,” Fuzzy Sets and Systems, vol. 158, no. 19, pp. 2153–2168, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Z. Zhao, Y. Cao, and Q. F. Tian, “Urban rail transit scheduling decision based on information fusion,” Journal of Jilin University (Engineering and Technology Edition), vol. 41, supplement 1, pp. 85–88, 2011. View at Google Scholar · View at Scopus
  32. G. Lebret, G. Yao, M. Ait-Ahmed, and T. Tang, “A gain-scheduling and intelligence fusion method for fault-tolerant control,” IFAC Proceedings, vol. 6, no. 1, pp. 1258–1263, 2006. View at Google Scholar
  33. X. Li, H. Kang, and H. H. Chen, “Sensing workload scheduling in hierarchical sensor networks for data fusion applications,” in Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC '07), pp. 214–219, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. S. C. H. Lu, D. Ramaswamy, and P. R. Kumar, “Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants,” IEEE Transactions on Semiconductor Manufacturing, vol. 7, no. 3, pp. 374–388, 1994. View at Publisher · View at Google Scholar · View at Scopus
  35. S. D. Ashok and G. L. Samuel, “Least square curve fitting technique for processing time sampled high speed spindle data,” International Journal of Manufacturing Research, vol. 6, no. 3, pp. 256–276, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. T. Chen, “Dynamic fuzzy-neural fluctuation smoothing rule for jobs scheduling in a wafer fabrication factory,” Proceedings of the Institution of Mechanical Engineers I, vol. 223, no. 8, pp. 1081–1094, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. C. Wang, T. Chen, and C. W. Lin, “A slack-diversifying nonlinear fluctuation smoothing rule for job dispatching in a wafer fabrication factory,” Robotics and Computer Integrated Manufacturing, vol. 29, no. 3, pp. 41–47, 2013. View at Publisher · View at Google Scholar
  38. S. F. Liu and F. L. Chen, “A data clustering model for wafer yield loss in semiconductor manufacturing,” Journal of the Chinese Institute of Industrial Engineers, vol. 21, no. 4, pp. 328–338, 2004. View at Google Scholar · View at Scopus
  39. C. H. Wang, “Recognition of semiconductor defect patterns using spatial filtering and spectral clustering,” Expert Systems with Applications, vol. 34, no. 3, pp. 1914–1923, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. X. L. Xie and G. Beni, “A validity measure for fuzzy clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 841–847, 1991. View at Publisher · View at Google Scholar · View at Scopus