Parallel Composition of Scheduling Solvers

  • Daniel Fontaine
  • Laurent Michel
  • Pascal Van Hentenryck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9676)


Recent work in model combinators, as well as projects like G12 and SIMPL, achieved significant progress in automating the generation of complex and hybrid solvers from high-level model specifications. This paper extends model combinators into the scheduling domain. This is of particular interest as, today, both Constraint Programming (CP) and Mixed-Integer Programming (MIP) perform well on scheduling problems providing different capabilities and trade-offs. The ability to construct hybrid scheduling solvers to leverage the strengths of both technologies as well as multiple problem encodings through high-level model combinators provides new opportunities. Complex parallel hybrids can be synthesized with minimal effort on the part of the user and provide substantial performance benefits over standalone solvers.


  1. 1.
    Akgun, O., Miguel, I., Jefferson, C., Frisch, A., Hnich, B.: Extensible automated constraint modelling (2011)Google Scholar
  2. 2.
    Amadini, R., Gabbrielli, M., Mauro, J.: SUNNY-CP: a sequential CP portfolio solver. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC 2015, pp. 1861–1867. ACM, New York (2015)Google Scholar
  3. 3.
    De Moura, L., Bjørner, N.: Satisfiability modulo theories: introduction and applications. Commun. ACM 54(9), 69–77 (2011)CrossRefGoogle Scholar
  4. 4.
    Duck, G.J., De Koninck, L., Stuckey, P.J.: Cadmium: an implementation of ACD term rewriting. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 531–545. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Duck, G.J., Stuckey, P.J., Brand, S.: ACD term rewriting. In: Etalle, S., Truszczyński, M. (eds.) ICLP 2006. LNCS, vol. 4079, pp. 117–131. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Fazel-Zarandi, M.M., Beck, J.C.: Solving a location-allocation problem with logic-based benders’ decomposition. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 344–351. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Fontaine, D., Michel, L.: A high level language for solver independent model manipulation and generation of hybrid solvers. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 180–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Fontaine, D., Michel, L., Van Hentenryck, P.: Model combinators for hybrid optimization. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 299–314. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Frisch, A., Harvey, W., Jefferson, C., Martínez-Hernández, B., Miguel, I.: Essence: a constraint language for specifying combinatorial problems. Constraints 13, 268–306 (2008)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Hooker, J.N.: Logic-based benders decomposition. Math. Program. 96, 33–60 (2003)MathSciNetMATHGoogle Scholar
  11. 11.
    Hurley, B., Kotthoff, L., Malitsky, Y., O’Sullivan, B.: Proteus: a hierarchical portfolio of solvers and transformations. In: Simonis, H. (ed.) CPAIOR 2014. LNCS, vol. 8451, pp. 301–317. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  12. 12.
    Seldin, J.P., Hindley, J.R.: Lambda-Calculus and Combinators An Introduction, vol. 2. Cambridge University Press, Cambridge (2008)MATHGoogle Scholar
  13. 13.
    Kadioglu, S., O’Mahony, E., Refalo, P., Sellmann, M.: Incorporating variance in impact-based search. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 470–477. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Ku, W.-Y., Beck, J.C.: Revisiting off-the-shelf mixed integer programming and constraint programming models for job shop scheduling. Technical report, University of Toronto (2014).
  15. 15.
    Michel, L., See, A., Van Hentenryck, P.: Transparent parallelization of constraint programming. INFORMS J. Comput. 21(3), 363–382 (2009)CrossRefMATHGoogle Scholar
  16. 16.
    Michel, L., Van Hentenryck, P.: A decomposition-based implementation of search strategies. ACM Trans. Comput. Logic 5(2), 351–383 (2004)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Moisan, T., Gaudreault, J., Quimper, C.-G.: Parallel discrepancy-based search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 30–46. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Nasiri, M.M., Kianfar, F.: A guided tabu search/path relinking algorithm for the job shop problem. Int. J. Adv. Manuf. Technol. 58(9–12), 1105–1113 (2012)CrossRefGoogle Scholar
  19. 19.
    O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: 19th Irish Conference on AI (2008)Google Scholar
  20. 20.
    Pacino, D., Van Hentenryck, P.: Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. In: IJCAI, pp. 1997–2002 (2011)Google Scholar
  21. 21.
    Perron, L.: Search procedures and parallelism in constraint programming. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 346–360. Springer, Heidelberg (1999)Google Scholar
  22. 22.
    Pisinger, D., Ropke, S.: Large Neighborhood Search. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 399–419. Springer, New York (2010)CrossRefGoogle Scholar
  23. 23.
    Puchinger, J., Stuckey, P.J., Wallace, M., Brand, S.: From high-level model to branch-and-price solution in G12. In: Trick, M.A. (ed.) CPAIOR 2008. LNCS, vol. 5015, pp. 218–232. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Puchinger, J., Stuckey, P.J., Wallace, M.G., Brand, S.: Dantzig-wolfe decomposition and branch-and-price solving in G12. Constraints 16(1), 77–99 (2011)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Refalo, P.: Linear formulation of constraint programming models and hybrid solvers. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 369–383. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  26. 26.
    Régin, J.-C., Rezgui, M., Malapert, A.: Embarrassingly parallel search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 596–610. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  27. 27.
    Schrijvers, T., Tack, G., Wuille, P., Samulowitz, H., Stuckey, P.J.: Search combinators. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 774–788. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  28. 28.
    Schulte, C.: Parallel search made simple. In: Proceedings of TRICS, a Post-Conference Workshop of CP 2000, Singapore, September 2000Google Scholar
  29. 29.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  30. 30.
    Stuckey, P.J., de la Banda, M.G., Maher, M.J., Marriott, K., Slaney, J.K., Somogyi, Z., Wallace, M., Walsh, T.: The G12 project: mapping solver independent models to efficient solutions. In: Gabbrielli, M., Gupta, G. (eds.) ICLP 2005. LNCS, vol. 3668, pp. 9–13. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  31. 31.
    Van Hentenryck, P.: Parallel constraint satisfaction in logic programming: pre-liminary results of CHIP within PEPSys. In: Sixth International Conference onLogic Programming, Lisbon, Portugal, June 1989Google Scholar
  32. 32.
    Van Hentenryck, P., Michel, L.: Synthesis of constraint-based local search algorithms from high-level models. In: Proceedings of the National Conference on Artificial Intelligence, 1(CONF 22), pp. 273–279 (2007)Google Scholar
  33. 33.
    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)CrossRefGoogle Scholar
  34. 34.
    Vilím, P., Barták, R., Čepek, O.: Extension of o(n log n) filtering algorithms for the unary resource constraint to optional activities. Constraints 10(4), 403–425 (2005)MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    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, Heidelberg (2015)Google Scholar
  36. 36.
    Lin, X., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla: portfolio-based algorithm selection for sat. J. Artif. Int. Res. 32(1), 565–606 (2008)MATHGoogle Scholar
  37. 37.
    Yunes, T., Aron, I.D., Hooker, J.N.: An integrated solver for optimization problems. Oper. Res. 58(2), 342–356 (2010)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Daniel Fontaine
    • 1
  • Laurent Michel
    • 1
  • Pascal Van Hentenryck
    • 2
  1. 1.University of ConnecticutStorrsUSA
  2. 2.University of MichiganAnn ArborUSA

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