Advertisement

Hybrid Algorithms in Constraint Programming

  • Mark Wallace
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4651)

Abstract

This paper surveys hybrid algorithms from a constraint programming perspective. It introduces techniques used within a constructive search framework, such as propagation and linear relaxation, as well as techniques used in combination with search by repair.

Keywords

constraint programming hybrid algorithms search 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Azarmi, N., Abdul-Hameed, W.: Workforce scheduling with constraint logic programming. BT Technology Journal 13(1) (1995)Google Scholar
  2. Ansótegui, C., del Val, A., Dotú, I., Fernàndez, C., Manyà, F.: Modeling choices in quasigroup completion: Sat vs. csp. In: AAAI, pp. 137–142 (2004)Google Scholar
  3. Apt, K., Wallace, M.: Constraint Logic Programming Using ECLiPSe. Cambridge University Press, Cambridge (2006)Google Scholar
  4. Barnier, N., Brisset, P.: Combine and conquer: Genetic algorithm and cp for optimization. In: Maher, M.J., Puget, J.-F. (eds.) Principles and Practice of Constraint Programming - CP98. LNCS, vol. 1520, p. 463. Springer, Heidelberg (1998)Google Scholar
  5. Beldiceanu, N., Bourreau, E., Chan, P., Rivreau, D.: Partial search strategy in CHIP. In: Proceedings of the 2nd. International Conference on Meta-Heuristics (1997)Google Scholar
  6. Burke, E., Kendall, G. (eds.): Search Methodologies: Introductory Tutorials in Optimization and Decision Support Methodologies. Springer, Heidelberg (2006)Google Scholar
  7. Beck, C., Refalo, P.: A Hybrid Approach to Scheduling with Earliness and Tardiness Costs. Annals of Operations Research 118, 49–71 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  8. Cronholm, W., Ajili, F.: Hybrid branch-and-price for multicast network design. In: INOC 2005. Proceedings of the 2nd International Network Optimization Conference, pp. 796–802 (2005)Google Scholar
  9. Caseau, Y., Laburthe, F.: Heuristics for large constrained vehicle routing problems. Journal of Heuristics 5(3) (1999)Google Scholar
  10. Caseau, Y., Laburthe, F., Le Pape, C., Rottembourg, B.: Combining local and global search in a constraint programming environment. Knowl. Eng. Rev. 16(1), 41–68 (2001)zbMATHCrossRefGoogle Scholar
  11. Choi, K.M.F., Lee, J.H.M., Stuckey, P.J.: A lagrangian reconstruction of genet. Artif. Intell. 123(1-2), 1–39 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  12. Cheng, B.M.W., Lee, J.H.M., Wu, J.C.K.: Speeding up constraint propagation by redundant modeling. In: Principles and Practice of Constraint Programming, pp. 91–103 (1996)Google Scholar
  13. Darby-Dowman, K., Little, J., Mitra, G., Zaffalon, M.: Constraint logic programming and integer programming approaches and their collaboration in solving an assignment scheduling problem. Constraints 1(3), 245–264 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  14. Mehta, S., Demirkol, E., Uzsoy, R.: A computational study of shifting bottleneck procedures for shop scheduling problems. Journal of Heuristics 3(2), 1381–1231 (2004)Google Scholar
  15. Easton, K., Nemhauser, G., Trick, M.: Solving the Travelling Tournament Problem: A Combined Integer Programming and Constraint Programming Approach. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, Springer, Heidelberg (2003)Google Scholar
  16. El Sakkout, H., Wallace, M.: Probe backtrack search for minimal perturbation in dynamic scheduling. Constraints 5(4) (2000)Google Scholar
  17. Eremin, A., Wallace, M.: Hybrid benders decomposition algorithms in constraint logic programming. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 1–15. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. Focacci, F., Laburthe, F., Lodi, A.: Local search and constraint programming. In: Constraint and Integer Programming Toward a Unified Methodology, ch. 9. Operations Research/Computer Science Interfaces Series, vol. 27, Springer, Heidelberg (2004)Google Scholar
  19. Focacci, F., Lodi, A., Milano, M.: Exploiting relaxations in CP. In: Constraint and Integer Programming Toward a Unified Methodology, ch. 5. Operations Research/Computer Science Interfaces Series, vol. 27, Springer, Heidelberg (2004)Google Scholar
  20. Gaschnig, J.: A constraint satisfaction method for inference making. In: Proc. 12th Annual Allerton Conf. on Circuit System Theory, pp. 866–874, Univ. Illinois (1974)Google Scholar
  21. Gervet, C.: Large scale combinatorial optimization: A methodological viewpoint. DIMACS Series in Discrete Mathematics and Computers Science 57, 151–175 (2001)MathSciNetGoogle Scholar
  22. Henz, M.: Scheduling a major college basketball conference–revisited. Oper. Res. 49(1), 163–168 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  23. Harvey, W.D., Ginsberg, M.L.: Limited discrepancy search. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 607–615 (1995)Google Scholar
  24. Homberger, J., Gehring, H.: A two-phase hybrid metaheuristic for the vehicle routing problem with time windows. Eur. J. Oper. Res. 162, 220–238 (2005)zbMATHCrossRefGoogle Scholar
  25. Hooker, J.N., Osorio, M.A.: Mixed logical / linear programming. Discrete Applied Mathematics 96-97, 395–442 (1999)CrossRefMathSciNetGoogle Scholar
  26. Hooker, J.N., Ottosson, G.: Logic-based Benders decomposition. Mathematical Programming 96, 33–60 (2003)zbMATHMathSciNetGoogle Scholar
  27. Hooker, J.N.: A Hybrid Method for Planning and Scheduling. Constraints 10(4) (2005)Google Scholar
  28. Jain, V., Grossmann, I.: Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems. INFORMS Journal on Computing 13(4), 258–276 (2001)CrossRefMathSciNetGoogle Scholar
  29. Jussien, N., Lhomme, O.: Local search with constraint propagation and conflict-based heuristics. In: AAAI 2000. Proceedings of the Seventh National Conference on Artificial Intelligence, Austin, TX, USA, August 2000, pp. 169–174 (2000)Google Scholar
  30. Kamarainen, O., El Sakkout, H.: Local probing applied to scheduling. In: Principles and Practice of Constraint Programming, pp. 155–171 (2002)Google Scholar
  31. Korf, R.E., Zhang, W., Thayer, I., Hohwald, H.: Frontier search. J. ACM 52(5), 715–748 (2005)CrossRefMathSciNetGoogle Scholar
  32. Li, Y.: Directed Annealing Searc. In: Constraint Satisfaction and Optimisation. PhD thesis, IC-Parc (1997)Google Scholar
  33. Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling salesman problem. Operations Research 21, 498–516 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  34. Mladenović, N., Hansen, P.: Variable neighborhood search. Comps. in Opns. Res. 24, 1097–1100 (1997)CrossRefzbMATHGoogle Scholar
  35. Marriott, K., Stuckey, P., Wallace, M.: Constraint logic programming. In: Rossi, van Beek, Walsh (eds.) Handbook of Constraint Programming, Elsevier, Amsterdam (2006)Google Scholar
  36. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manage. Sci. 42(6), 797–813 (1996)zbMATHGoogle Scholar
  37. Ouaja, W., Richards, E.B.: A hybrid multicommodity routing algorithm for traffic engineering. Networks 43(3), 125–140 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  38. Pesant, G., Gendreau, M.: A constraint programming framework for local search methods. Journal of Heuristics 5(3), 255–279 (1999)zbMATHCrossRefGoogle Scholar
  39. Prestwich, S.: Three clp implementations of branch-and-bound optimization. In: Parallelism and Implementation of Logic and Constraint Logic Programming, vol. 2, Nova Science Publishers, Inc. (1999)Google Scholar
  40. Prestwich, S.: Combining the scalability of local search with the pruning techniques of systematic search. Annals of Operations Research 115 (2002)Google Scholar
  41. Pralet, C., Verfaillie, G.: Travelling in the world of local searches in the space of partial assignments. In: Régin, J.-C., Rueher, M. (eds.) CPAIOR 2004. LNCS, vol. 3011, pp. 240–255. Springer, Heidelberg (2004)Google Scholar
  42. ROADEF (2007)Google Scholar
  43. Rego, C., Roucairol, C.: A parallel tabu search algorithm using ejection chains for vehicle routing. In: Meta-Heuristics: Theory and Applications, Kluwer, Dordrecht (1996)Google Scholar
  44. Rodosek, R., Wallace, M.G.: A generic model and hybrid algorithm for hoist scheduling problems. In: Maher, M.J., Puget, J.-F. (eds.) Principles and Practice of Constraint Programming - CP98. LNCS, vol. 1520, pp. 385–399. Springer, Heidelberg (1998)Google Scholar
  45. Rodosek, R., Wallace, M.G., Hajian, M.: A new approach to integrating mixed integer programming with constraint logic programming. Annals of Operations research 86, 63–87 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  46. Riera, D., Yorke-Smith, N.: An Improved Hybrid Model for the Generic Hoist Scheduling Problem. Annals of Operations Research 115, 173–191 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  47. Smith, B., Brailsford, S., Hubbard, P., Williams, H.P.: The Progressive Party Problem: Integer Linear Programming and Constraint Programming Compared. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, Springer, Heidelberg (1995)Google Scholar
  48. Sellmann, M., Fahle, T.: Constraint programming based lagrangian relaxation for the automatic recording problem. Annals of Operations Research 118, 17–33 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  49. Norvig, P., Russell, S.: Informed Search and Exploration. In: Artificial Intelligence: A Modern Approach, ch. 4, Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  50. Shang, Y., Wah, B.: A discrete lagrangian-based global-search method for solving satisfiability problems. Journal of Global Optimization 12(1), 61–100 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  51. Sellmann, M., Zervoudakis, K., Stamatopoulos, P., Fahle, T.: Crew assignment via constraint programming: Integrating column generation and heuristic tree search. Annals of Operations Research 115, 207–226 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  52. Hajian, M.T., El-Sakkout, H.H., Wallace, M.G., Richards, E.B., Lever, J.M.: Towards a closer integration of finite domain propagation and simplex-based algorithms. Annals of Operations Research 81, 421–431 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  53. Van Hentenryck, P.: A gentle introduction to Numerica. Artificial Intelligence 103, 209–235 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  54. Van Hentenryck, P., Carillon, J.-P.: Generality versus specificity: An experience with ai and or techniques. In: AAAI, pp. 660–664 (1988)Google Scholar
  55. Van Hentenryck, P., Michel, L.: Localizer: A Modeling Language for Local Search. Constraints 5, 41–82 (2000)Google Scholar
  56. Van Hentenryck, P., Michel, L.: Constraint-Based Local Search. MIT Press, Cambridge (2005)Google Scholar
  57. Verfaillie, G., Schiex, T.: Solution reuse in dynamic constraint satisfaction problems. In: AAAI 1994. Proceedings of the twelfth national conference on Artificial intelligence, vol. 1, pp. 307–312 (1994)Google Scholar
  58. Voudouris, E., Tsang, E.P.K.: Guided Local Search. European Journal of Operational Research 113, 469–499 (1999)zbMATHCrossRefGoogle Scholar
  59. Williams, H.P.: Model Building in Mathematical Programming. Wiley, Chichester (1999)Google Scholar
  60. Wallace, M.G., Schimpf, J.: Finding the right hybrid algorithm - a combinatorial meta-problem. Annals of Mathematics and Artificial Intelligence 34(4), 259–269 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  61. Yunes, T.H., Moura, A.V., de Souza, C.C.: Hybrid Column Generation Approaches for Urban Transit Crew Management Problems. Transportation Science 39(2), 273–288 (2002)CrossRefGoogle Scholar
  62. Yokoo, M.: Weak-commitment search for solving constraint satisfaction problems. In: AAAI 1994. Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, pp. 313–318 (1994)Google Scholar
  63. Zhang, L., Madigan, C.F., Moskewicz, M.H., Malik, S.: Efficient conflict driven learning in a boolean satisfiability solver. In: ICCAD 2001. Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design, pp. 279–285. ACM Press, New York (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mark Wallace
    • 1
  1. 1.Monash University, Faculty of Information Technology, Building 63, Clayton, Vic. 3800Australia

Personalised recommendations