Skip to main content
Log in

New filtering algorithms for combinations of among constraints

  • Published:
Constraints Aims and scope Submit manuscript

Abstract

Several combinatorial problems, such as car sequencing and rostering, feature sequence constraints, restricting the number of occurrences of certain values in every subsequence of a given length. We present three new filtering algorithms for the sequence constraint, including the first that establishes domain consistency in polynomial time. The filtering algorithms have complementary strengths: One borrows ideas from dynamic programming; another reformulates it as a regular constraint; the last is customized. The last two algorithms establish domain consistency, and the customized one does so in polynomial time. We provide experimental results that demonstrate the practical usefulness of each. We also show that the customized algorithm applies naturally to a generalized version of the sequence constraint that allows subsequences of varied lengths. The significant computational advantage of using a single generalized sequence constraint over a semantically equivalent collection of among or sequence constraints is demonstrated empirically.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Apt, K. (2003). Principles of constraint programming. Cambridge: Cambridge University Press.

    Google Scholar 

  2. Beldiceanu, N., & Carlsson, M. (2001). Revisiting the cardinality operator and introducing the cardinality-path constraint family. In P. Codognet (Ed.), Proceedings of the 17th international conference on logic programming (ICLP 2001), LNCS (Vol. 2237, pp. 59–73). New York: Springer.

    Google Scholar 

  3. Beldiceanu, N., Carlsson, M., & Rampon, J.-X. (2005). Global constraint catalog. Technical Report T2005-08, SICS.

  4. Beldiceanu, N., & Contejean, E. (1994). Introducing global constraints in CHIP. Journal of Mathematical and Computer Modelling, 20(12), 97–123.

    Article  MATH  Google Scholar 

  5. Dechter, R. (2003). Constraint processing. San Francisco: Morgan Kaufmann.

    Google Scholar 

  6. Demassey, S., Pesant, G., & Rousseau, L.-M. (2006). A cost-regular based hybrid column generation approach. Constraints, 11, 315–333.

    Article  MATH  MathSciNet  Google Scholar 

  7. Dincbas, M., Simonis, H., & van Hentenryck, P. (1988). Solving the car-sequencing problem in constraint logic programming. In Y. Kodratoff (Ed.), Proceedings of the European conference on artificial intelligence (ECAI) (pp. 290–295).

  8. Gent, I., & Walsh, T. (1999). CSPLib: A benchmark library for constraints. Technical report, TR APES-09-1999. at http://www.csplib.org.

  9. Mohr, R., & Masini, G. (1988). Good old discrete relaxation. In European conference on artificial intelligence (ECAI) (pp. 651–656).

  10. Pesant, G. (2004). A regular language membership constraint for finite sequences of variables. In M. Wallace (Ed.), Proceedings of the tenth international conference on principles and practice of constraint programming (CP 2004), Lecture Notes in Computer Science (Vol. 3258, pp. 482–495). New York: Springer.

    Google Scholar 

  11. Régin, J.-C. (1996). Generalized arc consistency for global cardinality constraint. In Proceedings of the thirteenth national conference on artificial intelligence and eighth innovative applications of artificial intelligence conference (AAAI / IAAI) (Vol. 1, pp. 209–215). Cambridge: AAAI/MIT Press.

    Google Scholar 

  12. Régin, J.-C. (2005). Combination of among and cardinality constraints. In R. Barták, & M. Milano (Eds.), Proceedings of the second international conference on integration of AI and OR techniques in constraint programming for combinatorial optimization problems (CP-AI-OR 2005), lecture notes in computer science (Vol. 3524, pp. 288–303). New York: Springer.

    Google Scholar 

  13. Régin, J.-C., & Puget, J.-F. (1997). A filtering algorithm for global sequencing constraints. In G. Smolka (Ed.), Proceedings of the third international conference on principles and practice of constraint programming (CP97), LNCS (Vol. 1330, pp. 32–46). New York: Springer.

    Chapter  Google Scholar 

  14. Trick, M. (2003). A dynamic programming approach for consistency and propagation for knapsack constraints. Annals of Operations Research, 118, 73–84.

    Article  MATH  MathSciNet  Google Scholar 

  15. van Hoeve, W.-J., Pesant, G., Rousseau, L.-M., & Sabharwal, A. (2006). Revisiting the sequence constraint. In CP-06: 12th international conference on principles and practice of constraint programming, lecture notes in computer science (Vol. 4204, pp. 620–634). Nantes, France, September.

  16. Zemmouri, T., Chan, P., Hiroux, M., & Weil, G. (2004). Multiple-level models: An application to employee timetabling. In E. K. Burke, & M. Trick (Eds.), Proceedings of the 5th international conference on the practice and theory of automated timetabling (PATAT’04) (pp. 397–412).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gilles Pesant.

Rights and permissions

Reprints and permissions

About this article

Cite this article

van Hoeve, WJ., Pesant, G., Rousseau, LM. et al. New filtering algorithms for combinations of among constraints. Constraints 14, 273–292 (2009). https://doi.org/10.1007/s10601-008-9067-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10601-008-9067-7

Keywords

Navigation