Robust and Parallel Solving of a Network Design Problem

Authors:
Claude Le Pape;Laurent Perron;Jean-Charles Régin;Paul Shaw
Affiliations:
-;-;-;-
Venue:
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Year:
2002

Citing 3
Cited 5

Network random keys: a tree representation scheme for genetic and evolutionary algorithms

Evolutionary Computation
Search Procedures and Parallelism in Constraint Programming

CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Exact solution of multicommodity network optimization problems with general step cost functions

Operations Research Letters

A CP-LP approach to network management in OSPF routing

Proceedings of the 2007 ACM symposium on Applied computing
Combining Tree Partitioning, Precedence, and Incomparability Constraints

Constraints
The "Not-Too-Heavy Spanning Tree" Constraint

CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Low-knowledge algorithm control

AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Constraint based resilience analysis

CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming

Quantified Score

Hi-index	0.00

Visualization

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.