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A comparison of different routing schemes for the robust network loading problem: polyhedral results and computation

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Abstract

We consider the capacity formulation of the Robust Network Loading Problem. The aim of the paper is to study what happens from the theoretical and from the computational point of view when the routing policy (or scheme) changes. The theoretical results consider static, volume, affine and dynamic routing, along with splittable and unsplittable flows. Our polyhedral study provides evidence that some well-known valid inequalities (the robust cutset inequalities) are facets for all the considered routing/flows policies under the same assumptions. We also introduce a new class of valid inequalities, the robust 3-partition inequalities, showing that, instead, they are facets in some settings, but not in others. A branch-and-cut algorithm is also proposed and tested. The computational experiments refer to the problem with splittable flows and the budgeted uncertainty set. We report results on several instances coming from real-life networks, also including historical traffic data, as well as on randomly generated instances. Our results show that the problem with static and volume routing can be solved quite efficiently in practice and that, in many cases, volume routing is cheaper than static routing, thus possibly representing the best compromise between cost and computing time. Moreover, unlikely from what one may expect, the problem with dynamic routing is easier to solve than the one with affine routing, which is hardly tractable, even using decomposition methods.

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Authors and Affiliations

  1. Istituto di Analisi dei Sistemi ed Informatica, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185, Rome, Italy

    Sara Mattia

  2. UMR CNRS 5506 LIRMM, Université de Montpellier, Rue Ada 161, 34095, Montpellier cedex 5, France

    Michael Poss

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  1. Sara Mattia
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Correspondence to Michael Poss.

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Mattia, S., Poss, M. A comparison of different routing schemes for the robust network loading problem: polyhedral results and computation. Comput Optim Appl 69, 753–800 (2018). https://doi.org/10.1007/s10589-017-9956-z

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  • DOI: https://doi.org/10.1007/s10589-017-9956-z

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