Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 478981, 27 pages
http://dx.doi.org/10.1155/2012/478981
Research Article

A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems

1Department of Management, College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China
2School of Software, Shenzhen Institute of Information Technology, Shenzhen 518029, China

Received 24 March 2012; Revised 24 May 2012; Accepted 25 May 2012

Academic Editor: Alex Elias-Zuniga

Copyright © 2012 Jian Xiong 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. R. Garey, D. S. Johnson, and R. Sethi, “The complexity of flowshop and job shop scheduling,” Mathematics of Operations Research, vol. 1, no. 2, pp. 117–129, 1976. View at Google Scholar · View at Scopus
  2. A. S. Jain and S. Meeran, “Deterministic job-shop scheduling: past, present and future,” European Journal of Operational Research, vol. 113, no. 2, pp. 390–434, 1999. View at Google Scholar · View at Scopus
  3. P. Brucker and R. Schlie, “Job-shop scheduling with multi-purpose machines,” Computing, vol. 45, no. 4, pp. 369–375, 1990. View at Publisher · View at Google Scholar · View at Scopus
  4. I. Kacem, S. Hammadi, and P. Borne, “Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems,” IEEE Transactions on Systems, Man and Cybernetics Part C, vol. 32, no. 1, pp. 1–13, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. I. Kacem, S. Hammadi, and P. Borne, “Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic,” Mathematics and Computers in Simulation, vol. 60, no. 3–5, pp. 245–276, 2002. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Xia and Z. Wu, “An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems,” Computers and Industrial Engineering, vol. 48, no. 2, pp. 409–425, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Hsu, R. Dupas, D. Jolly, and G. Goncalves, “Evaluation of mutation heuristics for the solving of multiobjective flexible job shop by an evolutionary algorithm,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 655–660, October 2002. View at Scopus
  8. G. Zhang, X. Shao, P. Li, and L. Gao, “An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem,” Computers and Industrial Engineering, vol. 56, no. 4, pp. 1309–1318, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Liu, A. Abraham, and Z. Wang, “A multi-swarm approach to multi-objective flexible job-shop scheduling problems,” Fundamenta Informaticae, vol. 95, no. 4, pp. 465–489, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Gao, M. Gen, L. Sun, and X. Zhao, “A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems,” Computers and Industrial Engineering, vol. 53, no. 1, pp. 149–162, 2007. 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 and Operations Research, vol. 35, no. 9, pp. 2892–2907, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. L. N. Xing, Y. W. Chen, and K. W. Yang, “Multi-objective flexible job shop schedule: design and evaluation by simulation modeling,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 362–376, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. L. N. Xing, Y. W. Chen, and K. W. Yang, “An efficient search method for multi-objective flexible job shop scheduling problems,” Journal of Intelligent Manufacturing, vol. 20, no. 3, pp. 283–293, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Q. Li, Q. K. Pan, and Y. C. Liang, “An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems,” Computers and Industrial Engineering, vol. 59, no. 4, pp. 647–662, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Bagheri, M. Zandieh, I. Mahdavi, and M. Yazdani, “An artificial immune algorithm for the flexible job-shop scheduling problem,” Future Generation Computer Systems, vol. 26, no. 4, pp. 533–541, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Vilcot and J.-C. Billaut, “A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem,” International Journal of Production Research, vol. 49, no. 23, pp. 6963–6980, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. N. B. Ho and J. C. Tay, “Solving multiple-objective flexible job shop problems by evolution and local search,” IEEE Transactions on Systems, Man and Cybernetics Part C, vol. 38, no. 5, pp. 674–685, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Frutos, A. C. Olivera, and F. Tohmé, “A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem,” Annals of Operations Research, vol. 181, no. 1, pp. 745–765, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Wang, L. Gao, C. Zhang, and X. Shao, “A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem,” International Journal of Advanced Manufacturing Technology, vol. 51, no. 5–8, pp. 757–767, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Q. Li, Q. K. Pan, and K. Z. Gao, “Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems,” International Journal of Advanced Manufacturing Technology, vol. 55, no. 9–12, pp. 1159–1169, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J.-Q. Li, Q.-K. Pan, and J. Chen, “A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems,” International Journal of Production Research, vol. 50, no. 4, pp. 1063–1078, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. 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
  24. E. Balas, “Machine sequencing via disjunctive graphs: an implicit enumeration algorithm,” Operations Research, vol. 17, pp. 941–957, 1969. View at Google Scholar
  25. K. Deb and S. Tiwari, “Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization,” European Journal of Operational Research, vol. 185, no. 3, pp. 1062–1087, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Ulrich, J. Bader, and E. Zitzler, “Integrating decision space diversity into hypervolume-based multiobjective search,” in Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference (GECCO '10), pp. 455–462, Portland, Ore, USA, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Ishibuchi, N. Akedo, and Y. Nojima, “A many-objective test problem for visually examining diversity maintenance behavior in a decision space,” in Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO '11), pp. 649–656, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Gen, Y. Tsujimura, and E. Kubota, “Solving job-shop scheduling problems by genetic algorithm,” in Proceedings of the 16th International Conference on Computer and Industrial Engineering, pp. 1577–1582, Ashikaga, Japan, October 1994. View at Scopus
  29. F. Pezzella, G. Morganti, and G. Ciaschetti, “A genetic algorithm for the Flexible Job-shop Scheduling Problem,” Computers and Operations Research, vol. 35, no. 10, pp. 3202–3212, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Hartmann and R. Kolisch, “Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 127, no. 2, pp. 394–407, 2000. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Kolisch and S. Hartmann, “Experimental investigation of heuristics for resource-constrained project scheduling: an update,” European Journal of Operational Research, vol. 174, no. 1, pp. 23–37, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. T. Murata, H. Ishibuchi, and H. Tanaka, “Genetic algorithms for flowshop scheduling problems,” Computers and Industrial Engineering, vol. 30, no. 4, pp. 1061–1071, 1996. View at Publisher · View at Google Scholar · View at Scopus
  33. T. Murata, H. Ishibuchi, and H. Tanaka, “Multi-objective genetic algorithm and its applications to flowshop scheduling,” Computers and Industrial Engineering, vol. 30, no. 4, pp. 957–968, 1996. View at Publisher · View at Google Scholar · View at Scopus
  34. J. Xiong, Y.-W. Chen, K.-W. Yang, Q.-S. Zhao, and L.-N. Xing, “A hybrid multiobjective genetic algorithm for robust resource-constrained project scheduling with stochastic durations,” Mathematical Problems in Engineering, vol. 2012, Article ID 786923, 24 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. S. Hartmann, “A competitive genetic algorithm for resource-constrained project scheduling,” Naval Research Logistics, vol. 45, no. 7, pp. 733–750, 1998. View at Google Scholar · View at Scopus
  36. S. Shadrokh and F. Kianfar, “A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty,” European Journal of Operational Research, vol. 181, no. 1, pp. 86–101, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. P. Moscato and R. Cheng, “A memetic approach for the traveling salesman problem: implementation of a computational ecology for combinatorial optimization on message-passing systems,” in Proceedings of the International Conference on Parallel Computing and Transputer Applications, Amsterdam, The Netherlands, 1992.
  38. H. Ishibuchi and T. Murata, “Multi-objective genetic local search algorithm,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '96), pp. 119–124, May 1996. View at Scopus
  39. H. Ishibuchi and T. Murata, “A multi-objective genetic local search algorithm and its application to flowshop scheduling,” IEEE Transactions on Systems, Man and Cybernetics Part C, vol. 28, no. 3, pp. 392–403, 1998. View at Google Scholar · View at Scopus
  40. C. Zhang, P. Li, Z. Guan, and Y. Rao, “A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem,” Computers and Operations Research, vol. 34, no. 11, pp. 3229–3242, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. H. Ishibuchi, T. Yoshida, and T. Murata, “Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 204–223, 2003. View at Publisher · View at Google Scholar · View at Scopus
  42. J.-Y. Lin and Y.-P. Chen, “Analysis on the collaboration between global search and local search in memetic computation,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 5, Article ID 6031912, pp. 608–623, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. H. Ishibuchi, Y. Hitotsuyanagi, Y. Wakamatsu, and Y. Nojima, “How to choose solutions for local search in multiobjective combinatorial memetic algorithms,” in Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN '10), 2010.
  44. N. Karimi, M. Zandieh, and H. R. Karamooz, “Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach,” Expert Systems with Applications, vol. 37, no. 6, pp. 4024–4032, 2010. View at Publisher · View at Google Scholar · View at Scopus
  45. S. H. A. Rahmati, M. Zandieh, and M. Yazdani, “Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem,” International Journal of Advanced Manufacturing Technology. In press. View at Publisher · View at Google Scholar · View at Scopus
  46. P. Brandimarte, “Routing and scheduling in a flexible job shop by tabu search,” Annals of Operations Research, vol. 41, no. 3, pp. 157–183, 1993. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Li, Q. Pan, and S. Xie, “An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems,” Applied Mathematics and Computation, vol. 218, no. 18, pp. 9353–9371, 2012. View at Publisher · View at Google Scholar · View at Scopus