Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2011, Article ID 390593, 20 pages
http://dx.doi.org/10.1155/2011/390593
Research Article

A Hybrid Differential Evolution and Tree Search Algorithm for the Job Shop Scheduling Problem

1School of Economics and Management, Nanchang University, Nanchang 330031, China
2Department of Automation, Tsinghua University, Beijing 100084, China

Received 15 July 2011; Accepted 26 August 2011

Academic Editor: Furong Gao

Copyright © 2011 Rui Zhang and Cheng Wu. 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. J. K. Lenstra, A. H. G. R. Rinnooy Kan, and P. Brucker, “Complexity of machine scheduling problems,” Annals of Discrete Mathematics, vol. 1, pp. 343–362, 1977. View at Publisher · View at Google Scholar · View at Scopus
  2. I. Essafi, Y. Mati, and S. Dauzère-Pérès, “A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem,” Computers and Operations Research, vol. 35, no. 8, pp. 2599–2616, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Zhou, W. Cheung, and L. C. Leung, “Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm,” European Journal of Operational Research, vol. 194, no. 3, pp. 637–649, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Nowicki and C. Smutnicki, “An advanced tabu search algorithm for the job shop problem,” Journal of Scheduling, vol. 8, no. 2, pp. 145–159, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Q. Li, Q. K. Pan, P. N. Suganthan, and T. J. Chua, “A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem,” The International Journal of Advanced Manufacturing Technology, vol. 52, no. 5–8, pp. 683–697, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Y. Sha and C.-Y. Hsu, “A hybrid particle swarm optimization for job shop scheduling problem,” Computers & Industrial Engineering, vol. 51, no. 4, pp. 791–808, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Moslehi and M. Mahnam, “A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search,” International Journal of Production Economics, vol. 129, no. 1, pp. 14–22, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Seo and D. Kim, “Ant colony optimisation with parameterised search space for the job shop scheduling problem,” International Journal of Production Research, vol. 48, no. 4, pp. 1143–1154, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. L. N. Xing, Y. W. Chen, P. Wang, Q. S. Zhao, and J. Xiong, “A knowledge-based ant colony optimization for flexible job shop scheduling problems,” Applied Soft Computing Journal, vol. 10, no. 3, pp. 888–896, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. K. L. Huang and C. J. Liao, “Ant colony optimization combined with taboo search for the job shop scheduling problem,” Computers and Operations Research, vol. 35, no. 4, pp. 1030–1046, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Gao, L. Sun, and M. Gen, “A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems,” Computers & Operations Research, vol. 35, no. 9, pp. 2892–2907, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Tavakkoli-Moghaddam, M. Azarkish, and A. Sadeghnejad-Barkousaraie, “A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem,” Expert Systems with Applications, vol. 38, no. 9, pp. 10812–10821, 2011. View at Publisher · View at Google Scholar
  13. M. Singer and M. Pinedo, “A computational study of bound techniques for the total weighted tardiness in job shops,” IIE Transactions, vol. 30, no. 2, pp. 109–118, 1998. View at Google Scholar
  14. E. Kutanoglu and I. Sabuncuoglu, “An analysis of heuristics in a dynamic job shop with weighted tardiness objectives,” International Journal of Production Research, vol. 37, no. 1, pp. 165–187, 1999. View at Google Scholar · View at Scopus
  15. S. J. Mason, J. W. Fowler, and W. M. Carlyle, “A modified shifting bottleneck heuristic for minimizing total weighted tardiness in complex job shops,” Journal of Scheduling, vol. 5, no. 3, pp. 247–262, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Mönch and R. Drießel, “A distributed shifting bottleneck heuristic for complex job shops,” Computers and Industrial Engineering, vol. 49, no. 3, pp. 363–380, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Kreipl, “A large step random walk for minimizing total weighted tardiness in a job shop,” Journal of Scheduling, vol. 3, no. 3, pp. 125–138, 2000. View at Google Scholar · View at Scopus
  18. M. Singer, “Decomposition methods for large job shops,” Computers & Operations Research, vol. 28, no. 3, pp. 193–207, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. K. M. J. Bontridder, “Minimizing total weighted tardiness in a generalized job shop,” Journal of Scheduling, vol. 8, no. 6, pp. 479–496, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Tavakkoli-Moghaddam, M. Khalili, and B. Naderi, “A hybridization of simulated annealing and electromagnetic-like mechanism for job shop problems with machine availability and sequence-dependent setup times to minimize total weighted tardiness,” Soft Computing, vol. 13, no. 10, pp. 995–1006, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Google Scholar · View at Scopus
  22. E. Mezura-Montes, J. Velázquez-Reyes, and C. A. Coello Coello, “Modified differential evolution for constrained optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 25–32, July 2006. View at Scopus
  23. L. Wang and L.-P. Li, “Fixed-structure H∞ controller synthesis based on differential evolution with level comparison,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 120–129, 2011. View at Publisher · View at Google Scholar
  24. M. Vasile, E. Minisci, and M. Locatelli, “An inflationary differential evolution algorithm for space trajectory optimization,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 2, pp. 267–281, 2011. View at Publisher · View at Google Scholar
  25. M. Sharma, M. Pandit, and L. Srivastava, “Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation,” International Journal of Electrical Power and Energy Systems, vol. 33, no. 3, pp. 753–766, 2011. View at Publisher · View at Google Scholar
  26. V. C. Mariani, L. S. Coelho, and P. K. Sahoo, “Modified differential evolution approaches applied in exergoeconomic analysis and optimization of a cogeneration system,” Expert Systems with Applications, vol. 38, no. 11, pp. 13886–13893, 2011. View at Publisher · View at Google Scholar
  27. N. Noman and H. Iba, “Accelerating differential evolution using an adaptive local search,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 107–125, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. C.-W. Chiang, W.-P. Lee, and J.-S. Heh, “A 2-Opt based differential evolution for global optimization,” Applied Soft Computing Journal, vol. 10, no. 4, pp. 1200–1207, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. W. Gong, Z. Cai, and L. Jiang, “Enhancing the performance of differential evolution using orthogonal design method,” Applied Mathematics and Computation, vol. 206, no. 1, pp. 56–69, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. G. Onwubolu and D. Davendra, “Scheduling flow shops using differential evolution algorithm,” European Journal of Operational Research, vol. 171, no. 2, pp. 674–692, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. B. Qian, L. Wang, R. Hu, W.-L. Wang, D.-X. Huang, and X. Wang, “A hybrid differential evolution method for permutation flow-shop scheduling,” The International Journal of Advanced Manufacturing Technology, vol. 38, no. 7, pp. 757–777, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. Q.-K. Pan, M. F. Tasgetiren, and Y.-C. Liang, “A discrete differential evolution algorithm for the permutation flowshop scheduling problem,” Computers & Industrial Engineering, vol. 55, no. 4, pp. 795–816, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Wang, Q.-K. Pan, P. N. Suganthan, W.-H. Wang, and Y.-M. Wang, “A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems,” Computers &Operations Research, vol. 37, no. 3, pp. 509–520, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. S. D. Wu, E. S. Byeon, and R. H. Storer, “A graph-theoretic decomposition of the job shop scheduling problem to achieve scheduling robustness,” Operations Research, vol. 47, no. 1, pp. 113–124, 1999. View at Google Scholar · View at Scopus
  35. K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, Springer, Berlin, Germany, 2005.
  36. C. Rego and R. Duarte, “A filter-and-fan approach to the job shop scheduling problem,” European Journal of Operational Research, vol. 194, no. 3, pp. 650–662, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. R. Bellman, “On a routing problem,” Quarterly of Applied Mathematics, vol. 16, no. 1, pp. 87–90, 1958. View at Publisher · View at Google Scholar · View at Scopus
  38. U. K. Chakraborty, Advances in Differential Evolution, Springer, Berlin, Germany, 2008.