Revisiting the Self-adaptive Large Neighborhood Search

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


This paper revisits the Self-Adaptive Large Neighborhood Search introduced by Laborie and Godard. We propose a variation in the weight-update mechanism especially useful when the LNS operators available in the portfolio exhibit unequal running times. We also propose some generic relaxations working for a large family of problems in a black-box fashion. We evaluate our method on various problem types demonstrating that our approach converges faster toward a selection of efficient operators.



We thank the reviewers for their feedback. This work was funded by the Walloon Region (Belgium) as part of the PRESupply project.


  1. 1.
    Puget, J.-F.: Constraint programming next challenge: simplicity of use. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 5–8. Springer, Heidelberg (2004). Scholar
  2. 2.
    Refalo, P.: Impact-based search strategies for constraint programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004). Scholar
  3. 3.
    Hebrard, E., Siala, M.: Explanation-based weighted degree. In: Salvagnin, D., Lombardi, M. (eds.) CPAIOR 2017. LNCS, vol. 10335, pp. 167–175. Springer, Cham (2017). Scholar
  4. 4.
    Gay, S., Hartert, R., Lecoutre, C., Schaus, P.: Conflict ordering search for scheduling problems. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 140–148. Springer, Cham (2015). Scholar
  5. 5.
    Chu, G., Stuckey, P.J.: Learning value heuristics for constraint programming. In: Michel, L. (ed.) CPAIOR 2015. LNCS, vol. 9075, pp. 108–123. Springer, Cham (2015). Scholar
  6. 6.
    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). Scholar
  7. 7.
    Pesant, G., Quimper, C.G., Zanarini, A.: Counting-based search: branching heuristics for constraint satisfaction problems. J. Artif. Intell. Res. 43, 173–210 (2012)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Vilím, P., Laborie, P., Shaw, P.: Failure-directed search for constraint-based scheduling. In: Michel, L. (ed.) CPAIOR 2015. LNCS, vol. 9075, pp. 437–453. Springer, Cham (2015). Scholar
  9. 9.
    Palmieri, A., Régin, J.-C., Schaus, P.: Parallel strategies selection. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 388–404. Springer, Cham (2016). Scholar
  10. 10.
    Picard-Cantin, É., Bouchard, M., Quimper, C.-G., Sweeney, J.: Learning the parameters of global constraints using branch-and-bound. In: Beck, J.C. (ed.) CP 2017. LNCS, vol. 10416, pp. 512–528. Springer, Cham (2017). Scholar
  11. 11.
    Beldiceanu, N., Simonis, H.: A model seeker: extracting global constraint models from positive examples. In: Milano, M. (ed.) CP 2012. LNCS, pp. 141–157. Springer, Heidelberg (2012). Scholar
  12. 12.
    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). Scholar
  13. 13.
    Malitsky, Y., Mehta, D., O’Sullivan, B., Simonis, H.: Tuning parameters of large neighborhood search for the machine reassignment problem. In: Gomes, C., Sellmann, M. (eds.) CPAIOR 2013. LNCS, vol. 7874, pp. 176–192. Springer, Heidelberg (2013). Scholar
  14. 14.
    Schaus, P., Van Hentenryck, P., Monette, J.N., Coffrin, C., Michel, L., Deville, Y.: Solving steel mill slab problems with constraint-based techniques: CP, LNS, and CBLS. Constraints 16(2), 125–147 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Jain, S., Van Hentenryck, P.: Large neighborhood search for dial-a-ride problems. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 400–413. Springer, Heidelberg (2011). Scholar
  16. 16.
    Bent, R., Van Hentenryck, P.: A two-stage hybrid local search for the vehicle routing problem with time windows. Transp. Sci. 38(4), 515–530 (2004)CrossRefGoogle Scholar
  17. 17.
    Godard, D., Laborie, P., Nuijten, W.: Randomized large neighborhood search for cumulative scheduling. In: Biundo, S., et al. (eds.) Proceedings of the International Conference on Automated Planning and Scheduling ICAPS-05, pp. 81–89. Citeseer (2005)Google Scholar
  18. 18.
    Carchrae, T., Beck, J.C.: Principles for the design of large neighborhood search. J. Math. Model. Algorithms 8(3), 245–270 (2009)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Gay, S., Schaus, P., De Smedt, V.: Continuous Casting Scheduling with Constraint Programming. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 831–845. Springer, Cham (2014). Scholar
  20. 20.
    Monette, J.N., Deville, Y., Van Hentenryck, P.: Aeon: synthesizing scheduling algorithms from high-level models. In: Chinneck, J.W., Kristjansson, B., Saltzman, M.J. (eds.) Operations Research and Cyber-Infrastructure. Research/Computer Science Interfaces, vol. 47, pp. 43–59. Springer, Boston (2009). Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    Laborie, P., Godard, D.: Self-adapting large neighborhood search: application to single-mode scheduling problems. Proceedings MISTA-07, Paris, vol. 8 (2007)Google Scholar
  23. 23.
    Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8), 2403–2435 (2007)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Lombardi, M., Schaus, P.: Cost impact guided LNS. In: Simonis, H. (ed.) CPAIOR 2014. LNCS, vol. 8451, pp. 293–300. Springer, Cham (2014). Scholar
  25. 25.
    Fleischmann, B.: The discrete lot-sizing and scheduling problem. Eur. J. Oper. Res. 44(3), 337–348 (1990)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Houndji, V.R., Schaus, P., Wolsey, L., Deville, Y.: The stockingcost constraint. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 382–397. Springer, Cham (2014). Scholar
  27. 27.
    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). Scholar
  28. 28.
    Monette, J.N., Schaus, P., Zampelli, S., Deville, Y., Dupont, P., et al.: A CP approach to the balanced academic curriculum problem. In: Seventh International Workshop on Symmetry and Constraint Satisfaction Problems, vol. 7 (2007)Google Scholar
  29. 29.
    Schaus, P., Deville, Y., et al.: A global constraint for bin-packing with precedences: application to the assembly line balancing problem. In: AAAI (2008)Google Scholar
  30. 30.
    Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proceedings of the 16th European Conference on Artificial Intelligence, pp. 146–150. IOS Press (2004)Google Scholar
  31. 31.
    Frost, D., Dechter, R.: In search of the best constraint satisfaction search (1994)Google Scholar
  32. 32.
    OscaR Team: OscaR: Scala in OR (2012).
  33. 33.
    Stuckey, P.J., Feydy, T., Schutt, A., Tack, G., Fischer, J.: The minizinc challenge 2008–2013. AI Mag. 35, 55–60 (2014)CrossRefGoogle Scholar
  34. 34.
    Boussemart, F., Lecoutre, C., Piette, C.: Xcsp3: an integrated format for benchmarking combinatorial constrained problems. arXiv preprint arXiv:1611.03398 (2016)

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ICTEAM instituteUniversite catholique de LouvainLouvain-la-NeuveBelgium

Personalised recommendations