Advertisement

Objective Landscapes for Constraint Programming

  • Philippe Laborie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)

Abstract

This paper presents the concept of objective landscape in the context of Constraint Programming. An objective landscape is a light-weight structure providing some information on the relation between decision variables and objective values, that can be quickly computed once and for all at the beginning of the resolution and is used to guide the search. It is particularly useful on decision variables with large domains and with a continuous semantics, which is typically the case for time or resource quantity variables in scheduling problems. This concept was recently implemented in the automatic search of CP Optimizer and resulted in an average speed-up of about 50% on scheduling problems with up to almost 2 orders of magnitude for some applications.

Keywords

Constraint Programming Scheduling Search Optimization 

References

  1. 1.
    Beck, J.C., Refalo, P.: A hybrid approach to scheduling with earliness and tardiness costs. Ann. Oper. Res. 118(1–4), 49–71 (2003)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Biskup, D., Feldmann, M.: Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates. Comput. Oper. Res. 28(8), 787–801 (2001)CrossRefGoogle Scholar
  3. 3.
    Greenberg, H., Pierskalla, W.: A review of quasi-convex functions. Oper. Res. 19(7), 1553–1570 (1971)CrossRefGoogle Scholar
  4. 4.
    Knopp, S., Dauzère-Pérès, S., Yugma, C.: Modeling maximum time lags in complex job-shops with batching in semiconductor manufacturing. In: Proceedings of the 15th International Conference on Project Management and Scheduling (PMS 2016), pp. 227–229 (2016)Google Scholar
  5. 5.
    Laborie, P., Godard, D.: Self-adapting large neighborhood search: application to single-mode scheduling problems. In: Baptiste, P., Kendall, G., Munier-Kordon, A., Sourd, F. (eds.) Proceedings of the 3rd Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA), Paris, France, pp. 276–284, 28–31 Aug 2007Google Scholar
  6. 6.
    Laborie, P., Rogerie, J., Shaw, P., Vilím, P.: IBM ILOG CP optimizer for scheduling. Constraints J. 23(2), 210–250 (2018)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Laborie, P., Rogerie, J.: Temporal linear relaxation in IBM ILOG CP optimizer. J. Sched. 19(4), 391–400 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Michel, L., Van Hentenryck, P.: Activity-based search for black-box constraint programming solvers. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 228–243. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29828-8_15CrossRefGoogle Scholar
  9. 9.
    Monette, J., Deville, Y., Hentenryck, P.V.: Just-in-time scheduling with constraint programming. In: Proceedibgs of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009) (2009)Google Scholar
  10. 10.
    Morton, T., Pentico, D.: Heuristic Scheduling Systems. Wiley, New York (1993)Google Scholar
  11. 11.
    Pesant, G.: Counting-based search for constraint optimization problems. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 3441–3447 (2016)Google Scholar
  12. 12.
    Refalo, P.: Impact-based search strategies for constraint programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30201-8_41CrossRefMATHGoogle Scholar
  13. 13.
    Vanhoucke, M.: A scatter search heuristic for maximising the net present value of a resource-constrained project with fixed activity cash flows. Int. J. Prod. Res. 48, 1983–2001 (2010)CrossRefGoogle Scholar
  14. 14.
    Vardi, M.Y.: Fundamentals of dependency theory. Technical report RJ4858, IBM Research (1985)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBMGentillyFrance

Personalised recommendations