Solving a Network Design Problem

Industrial optimization applications must be robust i.e., they 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 that addresses this requirement and its consequ...

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Bibliographic Details
Published inAnnals of operations research Vol. 130; no. 1-4; pp. 217 - 239
Main Authors Chabrier, Alain, Danna, Emilie, Le Pape, Claude, Perron, Laurent
Format Journal Article
LanguageEnglish
Published New York Springer Nature B.V 01.08.2004
Subjects
Algorithms
Case studies
Cost estimates
Design
Integer programming
Operations research
Optimization
Optimization techniques
Studies
Systems design
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Summary:Industrial optimization applications must be robust i.e., they 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 that addresses this requirement and its consequences on the applicability of different optimization techniques. An extensive benchmark suite, built on real network design data, is 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. [PUBLICATION ABSTRACT]
ISSN:0254-5330
1572-9338
DOI:10.1023/B:ANOR.0000032577.81139.84