Abstract
In Large Neighborhood Search (LNS) [14], a problem is solved by repeatedly exploring (via tree search) a neighborhood of an incumbent solution. Whenever an improving solution is found, this replaces the current incumbent. LNS can improve dramatically the scalability of CP on large real world problems, provided a good neighborhood selection heuristic is available. Unfortunately, designing a neighborhood heuristic for LNS is still largely an art and on many problems beating a random selection requires a considerable amount of both cleverness and domain knowledge. Recently, some authors have advocated the idea to include in the neighborhood the variables that are most directly affecting the cost of the current solution. The proposed approaches, however, are either domain dependent or require non-trivial solver modifications. In this paper, we rely on constraint propagation and basic solver support to design a set of simple, cost based, domain independent neighborhood selection heuristics. Those techniques are applied on Steel Mill Slab problems illustrating the superiority of some of them over pure random relaxations.
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References
Benichou, M., Gauthier, J.M., Girodet, P., Hentges, G., Ribiere, G., Vincent, O.: Experiments in mixed-integer linear programming. Mathematical Programming 1(1), 76–94 (1971)
Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 146–150 (2004)
Carchrae, T., Beck, J.C.: Principles for the design of large neighborhood search. Journal of Mathematical Modelling and Algorithms 8, 245–270 (2009)
Gargani, A., Refalo, P.: An efficient model and strategy for the steel mill slab design problem. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 77–89. Springer, Heidelberg (2007)
Laborie, P., Godard, D.: Self-adapting large neighborhood search: Application to single-mode scheduling problems. In: Proceedings MISTA-2007, Paris, pp. 276–284 (2007)
Mairy, J.-B.: Reinforced adaptive large neighborhood search. In: The Seventeenth International Conference on Principles and Practice of Constraint Programming (CP 2011), p. 55 (2011)
Mairy, J.-B., Schaus, P., Deville, Y.: Generic adaptive heuristics for large neighborhood search. In: Seventh International Workshop on Local Search Techniques in Constraint Satisfaction (LSCS 2010). A Satellite Workshop of CP (2010)
Michel, L., Van Hentenryck, P.: Activity-based search for black-box constraint programming solvers. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 228–243. Springer, Heidelberg (2012)
OscaR Team. OscaR: Scala in OR (2012), https://bitbucket.org/oscarlib/oscar
Shaw, P., Furnon, V.: Propagation guided large neighborhood search. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 468–481. Springer, Heidelberg (2004)
Refalo, P.: Impact-based search strategies for constraint programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004)
Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation science 40(4), 455–472 (2006)
Schaus, P., van Hentenryck, P., Monette, J.-N., Coffrin, C., Michel, L., Deville, Y.: Solving steel mill slab problems with constraint-based techniques: Cp, lns, and cbls. Constraints 16(2), 125–147 (2011)
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)
Zanarini, A., Pesant, G.: Solution counting algorithms for constraint-centered search heuristics. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 743–757. Springer, Heidelberg (2007)
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Lombardi, M., Schaus, P. (2014). Cost Impact Guided LNS. In: Simonis, H. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2014. Lecture Notes in Computer Science, vol 8451. Springer, Cham. https://doi.org/10.1007/978-3-319-07046-9_21
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DOI: https://doi.org/10.1007/978-3-319-07046-9_21
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