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dc.contributor.advisorPınar, Mustafa Ç.en_US
dc.contributor.authorAltın, Ayşegülen_US
dc.date.accessioned2016-01-08T18:02:57Z
dc.date.available2016-01-08T18:02:57Z
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/11693/14606
dc.descriptionAnkara : The Department of Industrial Engineering and The Institute of Engineering and Science of Bilkent Univ., 2007.en_US
dc.descriptionThesis (Ph.D.) -- Bilkent University, 2007.en_US
dc.descriptionIncludes bibliographical references leaves 160-166.en_US
dc.description.abstractIn this thesis, we study the design of networks robust to changes in demand estimates. We consider the case where the set of feasible demands is defined by an arbitrary polyhedron. Our motivation is to determine link capacity or routing configurations, which remain feasible for any realization in the corresponding demand polyhedron. We consider three well-known problems under polyhedral demand uncertainty all of which are posed as semi-infinite mixed integer programming problems. We develop explicit, compact formulations for all three problems as well as alternative formulations and exact solution methods. The first problem arises in the Virtual Private Network (VPN) design field. We present compact linear mixed-integer programming formulations for the problem with the classical hose traffic model and for a new, less conservative, robust variant relying on accessible traffic statistics. Although we can solve these formulations for medium-to-large instances in reasonable times using off-the-shelf MIP solvers, we develop a combined branch-and-price and cutting plane algorithm to handle larger instances. We also provide an extensive discussion of our numerical results. Next, we study the Open Shortest Path First (OSPF) routing enhanced with traffic engineering tools under general demand uncertainty with the motivation to discuss if OSPF could be made comparable to the general unconstrained routing (MPLS) when it is provided with a less restrictive operating environment. To the best of our knowledge, these two routing mechanisms are compared for the first time under such a general setting. We provide compact formulations for both routing types and show that MPLS routing for polyhedral demands can be computed in polynomial time. Moreover, we present a specialized branchand-price algorithm strengthened with the inclusion of cuts as an exact solution tool. Subsequently, we compare the new and more flexible OSPF routing with MPLS as well as the traditional OSPF on several network instances. We observe that the management tools we use in OSPF make it significantly better than the generic OSPF. Moreover, we show that OSPF performance can get closer to that of MPLS in some cases. Finally, we consider the Network Loading Problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity formulation of the problem, we prove an unexpected decomposition property obtained from projecting out the flow variables, considerably simplifying the resulting polyhedral analysis and computations by doing away with metric inequalities, an attendant feature of most successful algorithms on NLP. Under the hose model of feasible demands, we study the polyhedral aspects of NLP, used as the basis of an efficient branch-and-cut algorithm supported by a simple heuristic for generating upper bounds. We provide the results of extensive computational experiments on well-known network design instances.en_US
dc.description.statementofresponsibilityAltın, Ayşegülen_US
dc.format.extentxix, 167 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRobust network designen_US
dc.subjectBranchand-Priceen_US
dc.subjectNetwork Loading Problemen_US
dc.subjectOpen Shortest Path Firsten_US
dc.subjectVirtual Private Network,en_US
dc.subjectpolyhedral traffic uncertaintyen_US
dc.subject.lccHE336.A8 A48 2007en_US
dc.subject.lcshTraffic engineering Electronic data processing.en_US
dc.subject.lcshComputer networks.en_US
dc.titleRobust network design under polyhedral traffic uncertaintyen_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US


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