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,
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References
Aimms modelling system. http://business.aimms.com. Accessed July 2013
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.
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.
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.
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).
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.
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.
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.
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.
Fourer, R., Gay, D.M., Kernighan, B.W. (2002). AMPL: A modeling language for mathematical programming. Pacific Grove, CA: Duxbury Press.
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.
Ganu, H. (2011). Constraint programming. In ORMS today (pp. 44–47).
Guns, T., Nijssen, S., Raedt, L.D. (2011). Itemset mining: a constraint programming perspective. Artificial Intelligence, 175(12–13), 1951–1983.
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).
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.
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.
Mears, C., Garcia de la Banda, M., Wallace, M. (2009). On implementing symmetry detection. Constraints, 14(4), 443–477.
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.
Ohrimenko, O., Stuckey, P., Codish, M. (2009). Propagation via lazy clause generation. Constraints, 14(3), 357–391.
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.
Puchinger, J., Stuckey, P., Wallace, M., Brand, S. (2011). Dantzig-wolfe decomposition and branch-and-price solving in G12. Constraints, 16(1), 77–99.
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.
Schutt, A., Feydy, T., Stuckey, P., Wallace, M. (2011). Explaining the cumulative propagator. Constraints, 16(3), 250–282.
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).
Van Hentenryck, P. (1989). Constraint satisfaction in logic programming. Cambridge, MA: MIT Press.
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).
Van Hentenryck, P., Lustig, I., Michel, L., Puget, J.-F. (1999). The OPL optimization programming language. Cambridge, MA: MIT Press.
Van Hentenryck, P., & Michel, L. (2005). Constraint-based local search. Cambridge, MA: MIT Press.
Van Hentenryck, P., Perron, L., Puget, J.-F. (2000). Search and strategies in OPL. ACM TOCL, 1(2), 285–315.
Wallace, M., Novello, S., Schimpf, J. (1997). Eclipse: A platform for constraint logic programming. Technical report, IC-Parc Imperial College, London.
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.
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de la Banda, M.G., Stuckey, P.J., Van Hentenryck, P. et al. The future of optimization technology. Constraints 19, 126–138 (2014). https://doi.org/10.1007/s10601-013-9149-z
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DOI: https://doi.org/10.1007/s10601-013-9149-z