Abstract
Our research has been focused on developing cooperation techniques for solving large scale combinatorial optimisation problems using Constraint Programming with Local Search. In this paper, we introduce a framework for designing cooperative strategies. It is inspired from recent research carried out by the Constraint Programming community. For the tests that we present in this work we have selected two well known techniques: Forward Checking and Iterative Improvement. The set of benchmarks for the Capacity Vehicle Routing Problem shows the advantages to use this framework.
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References
Bockmayr, A., Kasper, T.: Branch-and-Infer: A unifying framework for integer and finite domain constraint programming. INFORMS J. Computing 10(3), 287–300 (1998); Also available as Technical Report MPI-I-97-2-008 of the Max Planck Institut für Informatik, Saarbrücken, Germany
Castro, C., Monfroy, E.: A Control Language for Designing Constraint Solvers. In: Bjorner, D., Broy, M., Zamulin, A.V. (eds.) PSI 1999. LNCS, vol. 1755, pp. 402–415. Springer, Heidelberg (2000)
Castro, C., Monfroy, E.: Basic Operators for Solving Constraints via Collaboration of Solvers. In: Campbell, J., Roanes-Lozano, E. (eds.) AISC 2000. LNCS (LNAI), vol. 1930, pp. 142–156. Springer, Heidelberg (2001)
Castro, C., Monfroy, E.: Towards a framework for designing constraint solvers and solver collaborations. Joint Bulletin of the Novosibirsk Computing Center (NCC) and the A. P. Ershov Institute of Informatics Systems (IIS). Series: Computer Science. Russian Academy of Sciences, Siberian Branch 16, 1–28 (December 2001)
Focacci, F., Laburthe, F., Lodi, A.: Constraint and Integer Programming: Toward a Unified Methodology. In: Local Search and Constraint Programming, November 2003, ch. 9, Kluwer, Dordrecht (2003)
Granvilliers, L., Monfroy, E., Benhamou, F.: Symbolic-Interval Cooperation in Constraint Programming. In: Proceedings of the 26th International Symposium on Symbolic and Algebraic Computation (ISSAC 2001), University of Western Ontario, London, Ontario, Canada, pp. 150–166. ACM Press, New York (2001)
Haralick, R.M., Elliot, G.L.: Increasing Tree Search Efficiency for Constraint Satisfaction Problems. Artificial Intelligence 14, 263–313 (1980)
Jussien, Lhomme: Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence 139, 21–45 (2002)
Kumar, V.: Algorithms for Constraint-Satisfaction Problems: A Survey. Artificial Intelligence Magazine 13(1), 32–44 (1992)
Mackworth, A.K.: Consistency in Networks of Relations. Artificial Intelligence 8, 99–118 (1977)
Marti, P., Rueher, M.: A Distributed Cooperating Constraints Solving System. International Journal of Artificial Intelligence Tools 4(1-2), 93–113 (1995)
Monfroy, E., Rusinowitch, M., Schott, R.: Implementing Non-Linear Constraints with Cooperative Solvers. In: George, K.M., Carroll, J.H., Oppenheim, D., Hightower, J. (eds.) Proceedings of ACM Symposium on Applied Computing (SAC 1996), Philadelphia, PA, USA, February 1996, pp. 63–72. ACM Press, New York (1996)
Prestwich: Combining the scalability of local search with the pruning techniques of systematic search. Annals of Operations Research 115, 51–72 (2002)
Solomon, M.: Algorithms for the vehicle routing and scheduling problem with time window constraints. Operations Research, 254–365 (1987)
Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1993)
Zahn, M., Hower, W.: Backtracking along with constraint processing and their time complexities. Journal of Experimental and Theoretical Artificial Intelligence 8, 63–74 (1996)
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Castro, C., Moossen, M., Riff, M.C. (2004). A Cooperative Framework Based on Local Search and Constraint Programming for Solving Discrete Global Optimisation. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_10
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DOI: https://doi.org/10.1007/978-3-540-28645-5_10
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