Skip to main content
SpringerLink
Log in
Book cover

International Conference on Principles and Practice of Constraint Programming

CP 2018: Principles and Practice of Constraint Programming pp 81–98Cite as

Solver-Independent Large Neighbourhood Search

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11008))

Abstract

The combination of large neighbourhood search (LNS) methods with complete search methods has proved to be very effective. By restricting the search to (small) areas around an existing solution, the complete method is often able to quickly improve its solutions. However, developing such a combined method can be time-consuming: While the model of a problem can be expressed in a high-level solver-independent language, the LNS search strategies typically need to be implemented in the search language of the target constraint solvers. In this paper we show how we can simplify this process by (a) extending constraint modelling languages to support solver-independent LNS search definitions, and (b) defining small solver extensions that allow solvers to implement these solver-independent LNS searches. Modellers can then implement an LNS search to be executed in any extended solver, by simply using the modelling language constructs. Experiments show that the resulting LNS searches only introduce a small overhead compared to direct implementations in the search language of the underlying solvers.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Random number functions need to be marked as ::impure for the compiler not to apply Common Subexpression Elimination (CSE) [23] if they are called multiple times with the same arguments.

  2. 2.

    Our implementations are available at https://github.com/Dekker1/{libminizinc,gecode,chuffed} on branches containing the keyword on_restart.

References

  1. Chiarandini, M., Gaspero, L.D., Gualandi, S., Schaerf, A.: The balanced academic curriculum problem revisited. J. Heuristics 18(1), 119–148 (2012)

    Article  Google Scholar 

  2. Chu, G.: Improving Combinatorial Optimization. Department of Computing and Information Systems, University of Melbourne (2011)

    Google Scholar 

  3. Cipriano, R., Di Gaspero, L., Dovier, A.: Gelato: a multi-paradigm tool for Large Neighborhood Search. In: Talbi, E.-G. (ed.) Hybrid Metaheuristics, pp. 389–414. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Danna, E., Perron, L.: Structured vs. Unstructured Large Neighborhood Search: A Case Study on Job-Shop Scheduling Problems with Earliness and Tardiness Costs. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 817–821. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45193-8_59

    Chapter  Google Scholar 

  5. Fourer, R., Gay, D., Kernighan, B.: AMPL: A Mathematical Programming Language. Manage. Sci. 36, 519–554 (1990)

    Article  Google Scholar 

  6. Frisch, A.M., Harvey, W., Jefferson, C., Martínez-Hernández, B., Miguel, I.: Essence: a constraint language for specifying combinatorial problems. Constraints 13(3), 268–306 (2008)

    Article  MathSciNet  Google Scholar 

  7. Gecode Team: Gecode: A Generic Constraint Development Environment (2016). http://www.gecode.org

  8. Google: or-tools (2017). https://developers.google.com/optimization/

  9. Michel, L., Van Hentenryck, P.: The Comet Programming Language and System. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 881–881. Springer, Heidelberg (2005). https://doi.org/10.1007/11564751_119

    Chapter  Google Scholar 

  10. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74970-7_38

  11. OscaR Team: OscaR: Scala in OR (2012). https://bitbucket.org/oscarlib/oscar

  12. Pacino, D., Van Hentenryck, P.: Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Three. IJCAI 11, pp. 1997–2002. AAAI Press, Barcelona (2011)

    Google Scholar 

  13. Perron, L., Shaw, P., Furnon, V.: Propagation guided large neighborhood search. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 468–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30201-8_35

    Chapter  Google Scholar 

  14. Pisinger, D., and Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8), 2403–2435 (2007)

    Article  MathSciNet  Google Scholar 

  15. Pisinger, D., and Ropke, S.: Large neighborhood search. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, pp. 399–419. Springer, Boston (2010). ISBN: 978-1-4419-1665-5. https://doi.org/10.1007/978-1-4419-1665-5_13

    Chapter  Google Scholar 

  16. Prud’homme, C., Fages, J.-G., Lorca, X.: Choco documentation. TASC - LS2N CNRS UMR 6241, COSLING S.A.S. (2017). http://www.choco-solver.org

  17. Prud’homme, C., Lorca, X., Jussien, N.: Explanation-Based Large Neighborhood Search. Constraints 19(4), 339–379 (2014)

    Article  MathSciNet  Google Scholar 

  18. Rendl, A., Guns, T., Stuckey, P.J., Tack, G.: MiniSearch: a solver-independent meta-search language for MiniZinc. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 376–392. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23219-5_27

    Google Scholar 

  19. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40(4), 455–472 (2006)

    Article  Google Scholar 

  20. 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). https://doi.org/10.1007/3-540-49481-2_30

    Chapter  Google Scholar 

  21. Stuckey, P.J., Becket, R., Fischer, J.: Philosophy of the MiniZinc challenge. Constraints 15(3), 307–316 (2010)

    Article  Google Scholar 

  22. Stuckey, P.J., Feydy, T., Schutt, A., Tack, G., Fischer, J.: The MiniZinc challenge 2008–2013. AI Mag. 35(2), 55–60 (2014)

    Article  Google Scholar 

  23. Stuckey, P.J., Tack, G.: MiniZinc with functions. In: Gomes, C., Sellmann, M. (eds.) CPAIOR 2013. LNCS, vol. 7874, pp. 268–283. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38171-3_18

    Chapter  Google Scholar 

  24. Van Hentenryck, P.: The OPL Optimization Programming Language. MIT Press, Cambridge (1999)

    Google Scholar 

  25. Van Hentenryck, P., Michel, L.: OPL script: composing and controlling models. In: Apt, K.R., Monfroy, E., Kakas, A.C., Rossi, F. (eds.) WC 1999. LNCS (LNAI), vol. 1865, pp. 75–90. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44654-0_4

    Chapter  Google Scholar 

  26. Van Hentenryck, P., Michel, L.: The Objective-CP optimization system. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 8–29. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40627-0_5

    Google Scholar 

Download references

Acknowledgements

This research was partly sponsored by the Australian Research Council grant DP180100151.

Author information

Authors and Affiliations

  1. Monash University, Melbourne, Australia

    Jip J. Dekker, Maria Garcia de la Banda, Peter J. Stuckey & Guido Tack

  2. Data61, CSIRO, Melbourne, Australia

    Jip J. Dekker, Andreas Schutt, Peter J. Stuckey & Guido Tack

Authors
  1. Jip J. Dekker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Garcia de la Banda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Schutt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter J. Stuckey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guido Tack
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jip J. Dekker .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    John Hooker

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dekker, J.J., de la Banda, M.G., Schutt, A., Stuckey, P.J., Tack, G. (2018). Solver-Independent Large Neighbourhood Search. In: Hooker, J. (eds) Principles and Practice of Constraint Programming. CP 2018. Lecture Notes in Computer Science(), vol 11008. Springer, Cham. https://doi.org/10.1007/978-3-319-98334-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98334-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98333-2

  • Online ISBN: 978-3-319-98334-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation