Advertisement

Abstract

Car sequencing is a well-known difficult problem. It has resisted and still resists the best techniques launched against it. Instead of creating a sophisticated search technique specifically designed and tuned for this problem, we will combine different simple local search-like methods using a portfolio of algorithms framework. In practice, we will base our solver on a powerful LNS algorithm and we will use the other local search-like algorithms as a diversification schema for it. The result is an algorithm is competitive with the best known approaches.

Keywords

Local Search Constraint Program Random Generator Network Design Problem Large Neighborhood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Parrello, B., Kabat, W., Wos, L.: Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem. Journal of Automated Reasoning 2, 1–42 (1986)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Dincbas, M., Simonis, H., Hentenryck, P.V.: Solving the car-sequencing problem in constraint logic programming. In: Kodratoff, Y. (ed.) Proceedings ECAI 1988, pp. 290–295 (1988)Google Scholar
  3. 3.
    Hentenryck, P.V., Simonis, H., Dincbas, M.: Constraint satisfaction using constraint logic programming. Artificial Intelligence 58, 113–159 (1992)CrossRefMathSciNetzbMATHGoogle Scholar
  4. 4.
    Smith, B.: Succeed-first or fail-first: A case study in variable and value ordering heuristics. In: Malyshkin, V.E. (ed.) PaCT 1997. LNCS, vol. 1277, pp. 321–330. Springer, Heidelberg (1997) (Presented at the ILOG Solver and ILOG Scheduler 2nd International Users’ Conference, Paris, July 1996) Google Scholar
  5. 5.
    Regin, J.C., Puget, J.F.: A filtering algorithm for global sequencing constraints. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 32–46. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  6. 6.
    Gent, I.: Two results on car sequencing problems. Technical Report APES-02- 1998, University of St. Andrews (1998)Google Scholar
  7. 7.
    Michel, L., Hentenryck, P.V.: A constraint-based architecture for local search. In: Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, pp. 83–100. ACM Press, New York (2002)CrossRefGoogle Scholar
  8. 8.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Bent, R., Hentenryck, P.V.: A two-stage hybrid local search for the vehicle routing problem with time windows. Technical Report CS-01-06, Brown University (2001)Google Scholar
  10. 10.
    Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA Journal on Computing 3, 149–156 (1991)zbMATHGoogle Scholar
  11. 11.
    J. Adams, E. B., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Management Science 34 (1988) 391–401 Google Scholar
  12. 12.
    Caseau, Y., Laburthe, F.: Effective forget-and-extend heuristics for scheduling problems. In: Proceedings of the First InternationalWorkshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimisation Problems (CP-AI-OR’99). (1999) Google Scholar
  13. 13.
    Palpant, M., Artigues, C., Michelon, P.: Solving the resource-constrained project scheduling problem by integrating exact resolution and local search. In: 8th International Workshop on Project Management and Scheduling PMS 2002. (2002) 289–292 Google Scholar
  14. 14.
    Le Pape, C., Perron, L., Régin, J.C., Shaw, P.: Robust and parallel solving of a network design problem. In Hentenryck, P.V., ed.: Proceedings of CP 2002, Ithaca, NY, USA (2002) 633–648 Google Scholar
  15. 15.
    Palpant, M., Artigues, C., Michelon, P.: A heuristic for solving the frequency assignment problem. In: XI Latin-Iberian American Congress of Operations Research (CLAIO). (2002) Google Scholar
  16. 16.
    Gomes, C. P., Selman, B.: Algorithm Portfolio Design: Theory vs. Practice. In: Proceedings of the Thirteenth Conference On Uncertainty in Artificial Intelligence (UAI-97), New Providence, Morgan Kaufmann (1997) Google Scholar
  17. 17.
    Gottlieb, J., Puchta, M., Solnon, C.: A study of greedy, local search and ant colony optimization approaches for car sequencing problems. In: Applications of evolutionary computing (EvoCOP 2003), Springer Verlag (2003) 246–257 LNCS 2611 Google Scholar
  18. 18.
    Chabrier, A., Danna, E., Le Pape, C., Perron, L.: Solving a network design problem. To appear in Annals of Operations Research, Special Issue following CP-AI-OR’2002 (2003) Google Scholar
  19. 19.
    Perron, L.: Fast restart policies and large neighborhood search. In: Proceedings of CPAIOR 2003. (2003) Google Scholar
  20. 20.
    Beck, J.C., Perron, L.: Discrepancy-Bounded Depth First Search. In: Proceedings of CP-AI-OR 00. (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Laurent Perron
    • 1
  • Paul Shaw
    • 1
  1. 1.ILOG SA 9 rue de VerdunGentilly cedexFrance

Personalised recommendations