Table of Contents
Journal of Industrial Engineering
Volume 2013, Article ID 295604, 11 pages
http://dx.doi.org/10.1155/2013/295604
Research Article

Chip Attach Scheduling in Semiconductor Assembly

Department of Industrial Engineering, School of Mechanical Engineering, Dongguan University of Technology, Songshan Lake District, Dongguan, Guangdong 523808, China

Received 13 December 2012; Accepted 20 February 2013

Academic Editor: Josefa Mula

Copyright © 2013 Zhicong Zhang et al. 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. M. X. Weng, J. Lu, and H. Ren, “Unrelated parallel machine scheduling with setup consideration and a total weighted completion time objective,” International Journal of Production Economics, vol. 70, no. 3, pp. 215–226, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Gairing, B. Monien, and A. Woclaw, “A faster combinatorial approximation algorithm for scheduling unrelated parallel machines,” in Automata, Languages and Programming, vol. 3580 of Lecture Notes in Computer Science, pp. 828–839, 2005. View at Google Scholar · View at Scopus
  3. G. Mosheiov, “Parallel machine scheduling with a learning effect,” Journal of the Operational Research Society, vol. 52, no. 10, pp. 1–5, 2001. View at Google Scholar · View at Scopus
  4. G. Mosheiov and J. B. Sidney, “Scheduling with general job-dependent learning curves,” European Journal of Operational Research, vol. 147, no. 3, pp. 665–670, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Yu, H. M. Shih, M. Pfund, W. M. Carlyle, and J. W. Fowler, “Scheduling of unrelated parallel machines: an application to PWB manufacturing,” IIE Transactions, vol. 34, no. 11, pp. 921–931, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. K. R. Baker and J. W. M. Bertrand, “A dynamic priority rule for scheduling against due-dates,” Journal of Operations Management, vol. 3, no. 1, pp. 37–42, 1982. View at Google Scholar · View at Scopus
  7. J. J. Kanet and X. Li, “A weighted modified due date rule for sequencing to minimize weighted tardiness,” Journal of Scheduling, vol. 7, no. 4, pp. 261–276, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. R. V. Rachamadugu and T. E. Morton, “Myopic heuristics for the single machine weighted tardiness problem,” Working Paper 28-81-82, Graduate School of Industrial Administration, Garnegie-Mellon University, 1981. View at Google Scholar
  9. A. Volgenant and E. Teerhuis, “Improved heuristics for the n-job single-machine weighted tardiness problem,” Computers and Operations Research, vol. 26, no. 1, pp. 35–44, 1999. View at Publisher · View at Google Scholar · View at Scopus
  10. D. C. Carroll, Heuristic sequencing of jobs with single and multiple components [Ph.D. thesis], Sloan School of Management, MIT, 1965.
  11. A. P. J. Vepsalainen and T. E. Morton, “Priority rules for job shops with weighted tardiness costs,” Management Science, vol. 33, no. 8, pp. 1035–1047, 1987. View at Google Scholar · View at Scopus
  12. R. S. Russell, E. M. Dar-El, and B. W. Taylor, “A comparative analysis of the COVERT job sequencing rule using various shop performance measures,” International Journal of Production Research, vol. 25, no. 10, pp. 1523–1540, 1987. View at Google Scholar · View at Scopus
  13. J. Bank and F. Werner, “Heuristic algorithms for unrelated parallel machine scheduling with a common due date, release dates, and linear earliness and tardiness penalties,” Mathematical and Computer Modelling, vol. 33, no. 4-5, pp. 363–383, 2001. View at Publisher · View at Google Scholar · View at Scopus
  14. C. F. Liaw, Y. K. Lin, C. Y. Cheng, and M. Chen, “Scheduling unrelated parallel machines to minimize total weighted tardiness,” Computers and Operations Research, vol. 30, no. 12, pp. 1777–1789, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. D. W. Kim, D. G. Na, and F. F. Chen, “Unrelated parallel machine scheduling with setup times and a total weighted tardiness objective,” Robotics and Computer-Integrated Manufacturing, vol. 19, no. 1-2, pp. 173–181, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, Mass, USA, 1998.
  17. C. J. C. H. Watkins, Learning from delayed rewards [Ph.D. thesis], Cambridge University, 1989.
  18. C. J. C. H. Watkins and P. Dayan, “Q-learning,” Machine Learning, vol. 8, no. 3-4, pp. 279–292, 1992. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Jaakkola, M. I. Jordan, and S. P. Singh, “On the convergence of stochastic iterative dynamic programming algorithms,” Neural Computation, vol. 6, pp. 1185–1201, 1994. View at Publisher · View at Google Scholar
  20. J. N. Tsitsiklis, “Asynchronous stochastic approximation and Q-learning,” Machine Learning, vol. 16, no. 3, pp. 185–202, 1994. View at Publisher · View at Google Scholar · View at Scopus
  21. D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, Belmont, Mass, USA, 1996.
  22. S. Riedmiller and M. Riedmiller, “A neural reinforcement learning approach to learn local dispatching policies in production scheduling,” in Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999.
  23. M. E. Aydin and E. Öztemel, “Dynamic job-shop scheduling using reinforcement learning agents,” Robotics and Autonomous Systems, vol. 33, no. 2, pp. 169–178, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Hong and V. V. Prabhu, “Distributed reinforcement learning control for batch sequencing and sizing in just-in-time manufacturing systems,” Applied Intelligence, vol. 20, no. 1, pp. 71–87, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. C. Wang and J. M. Usher, “Application of reinforcement learning for agent-based production scheduling,” Engineering Applications of Artificial Intelligence, vol. 18, no. 1, pp. 73–82, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. B. C. Csáji, L. Monostori, and B. Kádár, “Reinforcement learning in a distributed market-based production control system,” Advanced Engineering Informatics, vol. 20, no. 3, pp. 279–288, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. S. S. Singh, V. B. Tadić, and A. Doucet, “A policy gradient method for semi-Markov decision processes with application to call admission control,” European Journal of Operational Research, vol. 178, no. 3, pp. 808–818, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Kaya and R. Alhajj, “A novel approach to multiagent reinforcement learning: utilizing OLAP mining in the learning process,” IEEE Transactions on Systems, Man and Cybernetics Part C, vol. 35, no. 4, pp. 582–590, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. C. D. Paternina-Arboleda and T. K. Das, “A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem,” Simulation Modelling Practice and Theory, vol. 13, no. 5, pp. 389–406, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. C. E. Mariano-Romero, V. H. Alcocer-Yamanaka, and E. F. Morales, “Multi-objective optimization of water-using systems,” European Journal of Operational Research, vol. 181, no. 3, pp. 1691–1707, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. D. Vengerov, “A reinforcement learning framework for utility-based scheduling in resource-constrained systems,” Future Generation Computer Systems, vol. 25, no. 7, pp. 728–736, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Iwamura, N. Mayumi, Y. Tanimizu, and N. Sugimura, “A study on real-time scheduling for holonic manufacturing systems—determination of utility values based on multi-agent reinforcement learning,” in Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, pp. 135–144, Linz, Austria, 2009.
  33. M. Pinedo, Scheduling: Theory, Algorithms, and Systems, Prentice Hall, Englewoods Cliffs, NJ, USA, 2nd edition, 2002.