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

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      Author
      Altın, A.
      Yaman, H.
      Pınar, M. Ç.
      Date
      2011
      Journal 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
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      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/21678
      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.
      Published as
      http://dx.doi.org/10.1287/ijoc.1100.0380
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