Advertisement

Constraints

, Volume 19, Issue 2, pp 126–138 | Cite as

The future of optimization technology

  • Maria Garcia de la Banda
  • Peter J. Stuckey
  • Pascal Van Hentenryck
  • Mark Wallace
Article
  • 451 Downloads

Abstract

Technology for combinatorial optimization is rapidly changing, and as the size and scope of problems that can be solved steadily increases, the complexity of the underlying technology is growing. We foresee a huge demand for both the simplification of use of combinatorial optimization technology (so called “model and run” capabilities), as well as increasing need for the ability to quickly build complex hybrid solutions. These demands will place new emphasis on universal modeling languages and model transformation capabilities, as well as flexible and high level ways of specifying hybrid solutions. These changes put constraint programming in an ideal position since: constraint programming has the most high-level view of problems to begin with so we can ease modeling difficulties; and since constraint programming is an integrative technology, we have already spent considerable effort in making different solving technologies work together seamlessly. In this position paper we outline some of the key challenges and important research directions we foresee for optimization technology,

Keywords

Constraint programming Modelling languages Hybrid solving 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aimms modelling system. http://business.aimms.com. Accessed July 2013
  2. 2.
    Aron, I.D., Hooker, J.N., Yunes, T.H. (2004). Simpl: A system for integrating optimization techniques. In Integration of AI and OR techniques in constraint programming for combinatorial optimization problems, first international conference, CPAIOR 2004. Lecture notes in computer science (Vol. 3011, pp. 21–36). New York: Springer.Google Scholar
  3. 3.
    Beldiceanu, N., & Simonis, H. (2011). A constraint seeker: Finding and ranking global constraints from examples. In J.H.-M. Lee (Ed.), Principles and practice of constraint programming - CP 2011 - 17th international conference. Lecture notes in computer science (Vol. 6879, pp. 12–26). New York: Springer.CrossRefGoogle Scholar
  4. 4.
    Beldiceanu, N., & Simonis, H. (2012). A model seeker: Extracting global constraint models from positive examples. In M. Milano (Ed.), Proceedings of the 18th international conference of principles and practice of constraint programming, CP 2012. Lecture notes in computer science (Vol. 7514, pp. 141–157). New York: Springer.Google Scholar
  5. 5.
    Brodsky, A., & Nash, H. (2006). CoJava: Optimization modeling by nondeterministic simulation, in constraint programming. In Principles and practice of constraint programming (CP) (pp. 91–107).Google Scholar
  6. 6.
    Chu, G., Garcia de la Banda, M., Stuckey, P. (2010). Automatically exploiting subproblem equivalence in constraint programming. In Integration of AI and OR techniques in constraint programming for combinatorial optimization problems. LNCS (Vol. 6140, pp. 71–86). New York: Springer.CrossRefGoogle Scholar
  7. 7.
    Duck, G., De Koninck, L., Stuckey, P. (2008). Cadmium: An implementation of ACD term rewriting. In M. Garcia de la Banda, & E. Pontelli (Eds.), Proceedings of the 24th international conference on logic programming. LNCS (pp. 531–545). New York: Springer.Google Scholar
  8. 8.
    Elsayed, S., & Michel, L. (2011). Synthesis of search algorithms from high-level CP models. In J. Lee (Ed.), Proceedings of the 17th international conference on principles and practice of constraint programming. LNCS (Vol. 6876, pp. 256–270). New York: Springer.Google Scholar
  9. 9.
    Feydy, T., Somogyi, Z., Stuckey, P. (2011). Half-reification and flattening. In J. Lee (Ed.), Proceedings of the 17th international conference on principles and practice of constraint programming. LNCS (Vol. 6876, pp. 286–301). New York: Springer.Google Scholar
  10. 10.
    Fourer, R., Gay, D.M., Kernighan, B.W. (2002). AMPL: A modeling language for mathematical programming. Pacific Grove, CA: Duxbury Press.Google Scholar
  11. 11.
    Francis, K., Brand, S., Stuckey, P. (2012). Optimization modelling for software developers. In M. Milano (Ed.), Proceedings of the 18th international conference on principles and practice of constraint programming (page to appear). New York: Springer.Google Scholar
  12. 12.
    Ganu, H. (2011). Constraint programming. In ORMS today (pp. 44–47).Google Scholar
  13. 13.
    Guns, T., Nijssen, S., Raedt, L.D. (2011). Itemset mining: a constraint programming perspective. Artificial Intelligence, 175(12–13), 1951–1983.CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Harvey, W., & Kelsey, T. (2003). Symmetry group expression for CSPs. In Proceedings of Sym-Con03: Third international workshop on symmetry in constraint satisfaction problems (pp. 86–96).Google Scholar
  15. 15.
    Junker, U. (2004). Quickxplain: Preferred explanations and relaxations for over-constrained problems. In Proceedings of the nineteenth national conference on artificial intelligence, sixteenth conference on innovative applications of artificial intelligence (pp. 167–172). AAAI Press/The MIT Press.Google Scholar
  16. 16.
    Marriott, K., Nethercote, N., Rafeh, R., Stuckey, P., Garcia de la Banda, M., Wallace, M. (2008). The design of the Zinc modelling language. Constraints, 13(3), 229–267.CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Mears, C., Garcia de la Banda, M., Wallace, M. (2009). On implementing symmetry detection. Constraints, 14(4), 443–477.CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    Monette, J.-N., Deville, Y., Van Hentenryck, P. (2009). Aeon: Synthesizing Scheduling Algorithms from High-level Models (pp. 43–59). Operations Research/Computer Science Interfaces. New York: Springer.Google Scholar
  19. 19.
    Ohrimenko, O., Stuckey, P., Codish, M. (2009). Propagation via lazy clause generation. Constraints, 14(3), 357–391.CrossRefMATHMathSciNetGoogle Scholar
  20. 20.
    Perron, L. (1999). Search procedures and parallelism in constraint programming. In J. Jaffar (Ed.), Fifth international conference on principles and practice of constraint programming. LNCS (Vol. 1713, pp. 346–360). New York: Springer.Google Scholar
  21. 21.
    Puchinger, J., Stuckey, P., Wallace, M., Brand, S. (2011). Dantzig-wolfe decomposition and branch-and-price solving in G12. Constraints, 16(1), 77–99.CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Schrijvers, T., Tack, G., Wuille, P., Samulowitz, H., Stuckey, P. (2011). Search combinators. In J. Lee (Ed.), Seventeenth international conference on principles and practice of constraint programming. LNCS (Vol. 6876, pp. 774–788). New York: Springer.Google Scholar
  23. 23.
    Schutt, A., Feydy, T., Stuckey, P., Wallace, M. (2011). Explaining the cumulative propagator. Constraints, 16(3), 250–282.CrossRefMATHMathSciNetGoogle Scholar
  24. 24.
    Trick, M. (2005). Formulations and reformulations in integer programming. In Proceedings of the second international conference on the integration of AI and OR techniques in constraint programming for combinatorial optimization problems (CP-AI-OR’05).Google Scholar
  25. 25.
    Van Hentenryck, P. (1989). Constraint satisfaction in logic programming. Cambridge, MA: MIT Press.Google Scholar
  26. 26.
    Van Hentenryck, P., Flener, P., Pearson, J., Agren, M. (2005). Compositional derivation of symmetries for constraint satisfaction. In Proceedings of the 6th international symposium on abstraction, reformulation and approximation, (SARA 2005) (pp. 234–247).Google Scholar
  27. 27.
    Van Hentenryck, P., Lustig, I., Michel, L., Puget, J.-F. (1999). The OPL optimization programming language. Cambridge, MA: MIT Press.Google Scholar
  28. 28.
    Van Hentenryck, P., & Michel, L. (2005). Constraint-based local search. Cambridge, MA: MIT Press.Google Scholar
  29. 29.
    Van Hentenryck, P., Perron, L., Puget, J.-F. (2000). Search and strategies in OPL. ACM TOCL, 1(2), 285–315.CrossRefMathSciNetGoogle Scholar
  30. 30.
    Wallace, M., Novello, S., Schimpf, J. (1997). Eclipse: A platform for constraint logic programming. Technical report, IC-Parc Imperial College, London.Google Scholar
  31. 31.
    Xie, F., & Davenport, A.J. (2010). Massively parallel constraint programming for supercomputers: Challenges and initial results. In Integration of AI and OR techniques in constraint programming for combinatorial optimization problems. LNCS (Vol. 6140, pp. 334–338). New York: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Maria Garcia de la Banda
    • 1
  • Peter J. Stuckey
    • 2
  • Pascal Van Hentenryck
    • 2
  • Mark Wallace
    • 1
  1. 1.National ICT Australia and Monash UniversityVictoriaAustralia
  2. 2.National ICT Australia and the University of MelbourneVictoriaAustralia

Personalised recommendations