The robust network loading problem under hose demand uncertainty: formulation, polyhedral analysis, and computations

Date
2011
Authors
Altın, A.
Yaman, H.
Pınar, M. Ç.
Advisor
Supervisor
Co-Advisor
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Instructor
Source Title
INFORMS Journal on Computing
Print ISSN
1091-9856
Electronic ISSN
1526-5528
Publisher
Institute for Operations Research and the Management Sciences (I N F O R M S)
Volume
23
Issue
1
Pages
75 - 89
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
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Abstract

We consider the network loading problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity flow formulation of the problem, we state a decomposition property obtained from projecting out the flow variables. This property considerably simplifies the resulting polyhedral analysis and computations by doing away with metric inequalities. Then we focus on a specific choice of the uncertainty description, called the "hose model," which specifies aggregate traffic upper bounds for selected endpoints of the network. We study the polyhedral aspects of the NLP under hose demand uncertainty and use the results as the basis of an efficient branch-and-cut algorithm. The results of extensive computational experiments on well-known network design instances are reported.

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Keywords
Branch and cut, Hose model, Network loading problem, Polyhedral analysis, Polyhedral demand uncertainty, Robust optimization
Citation
Published Version (Please cite this version)