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Combining constraint Propagationand meta-heuristics for searching a Maximum Weight Hamiltonian Chain

Published online by Cambridge University Press:  12 October 2006

Yves Caseau
Yves Caseau*
Affiliation:
Bouygues e-Lab, 1 avenue Eugène Freyssinet, 78061 St-Quentin en Yvelines Cedex, France; e-mail: [email protected]
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Abstract

This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy search and the introduction of randomization and de-randomization within our combination algebra. This algebra is made of terms that represent optimization algorithms, following the approach of SALSA [1], which can be generated or tuned automatically using a learning meta-strategy [2]. In this paper, the hybrid combination that is investigated mixes constraint propagation, a special form of limited discrepancy search and large neighborhood search.

Type
Research Article
Information
RAIRO - Operations Research , Volume 40 , Issue 2: ROADEF 03 , April 2006 , pp. 77 - 95
Copyright
© EDP Sciences, 2006

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References

Laburthe, F. and Caseau, Y., SALSA: A Language for Search Algorithms, in Proc. of CP'98, edited by M. Maher, J.-F. Puget, Springer. Lect. Notes Comput. Sci. 1520 (1998) 310–324. CrossRef
Caseau, Y., Silverstein, G. and Laburthe, F., Learning Hybrid Algorithms for Vehicle Routing Problems. TPLP 1 (2001) 779–806.
Minton, S., Configurable Solvers: Tailoring General Methods to Specific Applications, in Proc. of CP'97, edited by G. Smolka, Springer. Lect. Notes Comput. Sci. 1330 (1997) 372–374. CrossRef
Y. Caseau and F. Laburthe, Solving small TSPs with Constraints, in Proc. of the 14th International Conference on Logic Programming. The MIT Press (1997).
F. Focacci, M. Milano and A. Lodi, Solving TSP with Time Windows with Constraints. ICLP 515–529W (1999).
Y. Caseau, G. Silverstein and F. Laburthe, A Meta-Heuristic Factory for Vehicle Routing Problems, in Proc. of CP'99, Lect. Notes Comput. Sci. 1713 (1999).
Harvey and M. Ginsberg, Limited Discrepancy Search, in Proc. of the 14th IJCAI. Morgan Kaufmann (1995) 607–615.
P. Shaw, Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems, in Proc. of CP'98, Lect. Notes Comput. Sci. 1520 (1998).
T. Benoist and B. Rottembourg, Upper Bounds of the Maximal Revenue of an Earth Observation Satellite, in 4OR: Quart. J. Belgian, French and Italian Oper. Res. Soc., Vol. 2, Issue 3, Oct. 2004.
2003 ROADEF Challenge: http://www.prism.uvsq.fr/~vdc/ROADEF/CHALLENGES/2003.
E. Taillard, P. Badeau, M. Gendreau, F. Guertin and J.-Y. Potvin, A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows, Transportation Science 31 (1997).
D. Martin and P. Shmoys, A time-based approach to the Jobshop problem, in Proc. of IPCO'5, edited by M. Queyranne, Lect. Comput. Notes Sci. 1084 (1996).
Caseau, Y. and Laburthe, F., Improving Branch and Bound for Jobshop Scheduling with Constraint Propagation. Combin. Comput. Sci. 1995 (1995) 129–149.
D. Applegate and B. Cook. A Computational Study of the Job Shop Scheduling Problem. Oper. Res. Soci. Amer. 3 (1991).
Caseau, Y., Josset, F.X. and Laburthe, F., CLAIRE: Combining sets, search and rules to better express algorithms. TPLP 2 (2002) 769–805.