Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7123
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dc.contributor.advisorMitra, G-
dc.contributor.authorGulpinar, Nalan-
dc.date.accessioned2013-01-14T12:47:13Z-
dc.date.available2013-01-14T12:47:13Z-
dc.date.issued1998-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/7123-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.en_US
dc.description.abstractLinear programming (LP) models that contain a (substantial) network structure frequently arise in many real life applications. In this thesis, we investigate two main questions; i) how an embedded network structure can be detected, ii) how the network structure can be exploited to create improved sparse simplex solution algorithms. In order to extract an embedded pure network structure from a general LP problem we develop two new heuristics. The first heuristic is an alternative multi-stage generalised upper bounds (GUB) based approach which finds as many GUB subsets as possible. In order to identify a GUB subset two different approaches are introduced; the first is based on the notion of Markowitz merit count and the second exploits an independent set in the corresponding graph. The second heuristic is based on the generalised signed graph of the coefficient matrix. This heuristic determines whether the given LP problem is an entirely pure network; this is in contrast to all previously known heuristics. Using generalised signed graphs, we prove that the problem of detecting the maximum size embedded network structure within an LP problem is NP-hard. The two detection algorithms perform very well computationally and make positive contributions to the known body of results for the embedded network detection. For computational solution a decomposition based approach is presented which solves a network problem with side constraints. In this approach, the original coefficient matrix is partitioned into the network and the non-network parts. For the partitioned problem, we investigate two alternative decomposition techniques namely, Lagrangean relaxation and Benders decomposition. Active variables identified by these procedures are then used to create an advanced basis for the original problem. The computational results of applying these techniques to a selection of Netlib models are encouraging. The development and computational investigation of this solution algorithm constitute further contribution made by the research reported in this thesis.en_US
dc.description.sponsorshipThis study is funded by the Turkish Educational Council and Mugla University.en_US
dc.language.isoenen_US
dc.publisherBrunel University, School of Information Systems, Computing and Mathematics-
dc.relation.ispartofSchool of Information Systems, Computing and Mathematics-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/7123/1/FulltextThesis.pdf-
dc.titleAnalysis of large scale linear programming problems with embedded network structures: Detection and solution algorithmsen_US
dc.typeThesisen_US
Appears in Collections:Business and Management
Dept of Mathematics Theses
Mathematical Sciences

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