Abstract
Industrial optimization applications must be “robust,” i.e., must provide good solutions to problem instances of different size and numerical characteristics, and continue to work well when side constraints are added. This paper presents a case study in which this requirement and its consequences on the applicability of different optimization techniques have been addressed. An extensive benchmark suite, built on real network design data provided by France Telecom R&D, has been used to test multiple algorithms for robustness against variations in problem size, numerical characteristics, and side constraints. The experimental results illustrate the performance discrepancies that have occurred and how some have been corrected. In the end, the results suggest that we shall remain very humble when assessing the adequacy of a given algorithm for a given problem, and that a new generation of public optimization benchmark suites is needed for the academic community to attack the issue of algorithm robustness as it is encountered in industrial settings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
J. Christopher Beck and Laurent Perron. Discrepancy-Bounded Depth First Search. In Proceedings of CP-AI-OR 00, March 2000.
Raphaël Bernhard, Jacques Chambon, Claude Lepape, Laurent Perron, and Jean Charles Régin. Résolution d’un probléme de conception de réseau avec parallel solver. In Proceeding of JFPLC, 2002. (In French).
Daniel Bienstock and Oktay Günlük. Capacitated network design: Polyhedral structure and computation. ORS A J, 1996:243–260, 1996.
Cplex. ILOG CPLEX 7.5 User’s Manual and Reference Manual. ILOG, S.A., 2001.
V. Gabrel, A. Knippel, and M. Minoux. Exact solution of multicommodity network optimization problems with general step cost functions. Operations Research Letters, 25:15–23, 1999.
Michel Gondran and Michel Minoux. Graphes et algorithmes. Eyrolles, 1995.
Laurent Perron. Search procedures and parallelism in constraint programming. In Joxan Jaffar, editor, Proceedings of CP’ 99, pages 346–360. Springer-Verlag, 1999.
Laurent Perron. Practical parallelism in constraint programming. In Proceedings of CP-AI-OR 2002, pages 261–276, March 2002.
Franz Rothlauf, David E. Goldberg, and Armin Heinzl. Network random keys: A tree representation scheme for genetic and evolutionary algorithms. Evolutionary Computation, 10(1):75–97, 2002.
P. Shaw, V. Furnon, and B. de Backer. A lightweight addition to CP frameworks for improved local search. In Ulrich Junker, Stefan E. Karisch, and Torsten Fahle, editors, Proceedings of CP-AI-OR 2000, 2000.
Paul Shaw, Vincent Furnon, and Bruno De Backer. A constraint programming toolkit for local search. In Stefan Voss and David L. Woodruff, editors, Optimization Software Class Libraries, pages 219–262. Kluwer Academic Publishers, 2002.
Solver. ILOG Solver 5.2 User’s Manual and Reference Manual. ILOG, S. A., 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Le Pape, C., Perron, L., Régin, JC., Shaw, P. (2002). Robust and Parallel Solving of a Network Design Problem. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_42
Download citation
DOI: https://doi.org/10.1007/3-540-46135-3_42
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44120-5
Online ISBN: 978-3-540-46135-7
eBook Packages: Springer Book Archive