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dc.contributor.advisorPeters, Brett A.en_US
dc.creatorRagab, Ayman Hassanen_US
dc.date.accessioned2012-02-14T22:19:00Zen_US
dc.date.accessioned2012-02-16T16:14:22Z
dc.date.available2012-02-14T22:19:00Zen_US
dc.date.available2012-02-16T16:14:22Z
dc.date.created2010-12en_US
dc.date.issued2012-02-14en_US
dc.date.submittedDecember 2010en_US
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8973en_US
dc.description.abstractSupply chain network design (SCND) identifies the production and distribution resources essential to maximizing a network’s profit. Once implemented, a SCND impacts a network’s performance for the long-term. This dissertation extends the SCND literature both in terms of model scope and solution approach. The SCND problem can be more realistically modeled to improve design decisions by including: the location, capacity, and technology attributes of a resource; the effect of the economies of scale on the cost structure; multiple products and multiple levels of supply chain hierarchy; stochastic, dynamic, and correlated demand; and the gradually unfolding uncertainty. The resulting multistage stochastic mixed-integer program (MSMIP) has no known general purpose solution methodology. Two decomposition approaches—end-of-horizon (EoH) decomposition and nodal decomposition—are applied. The developed EoH decomposition exploits the traditional treatment of the end-of-horizon effect. It rests on independently optimizing the SCND of every node of the last level of the scenario-tree. Imposing these optimal configurations before optimizing the design decisions of the remaining nodes produces a smaller and thus easier to solve MSMIP. An optimal solution results when the discount rate is 0 percent. Otherwise, this decomposition deduces a bound on the optimality-gap. This decomposition is neither SCND nor MSMIP specific; it pertains to any application sensitive to the EoH-effect and to special cases of MSMIP. To demonstrate this versatility, additional computational experiments for a two-stage mixed-integer stochastic program (SMIP) are included. This dissertation also presents the first application of nodal decomposition in both SCND and MSMIP. The developed column generation heuristic optimizes the nodal sub-problems using an iterative procedure that provides a restricted master problem’s columns. The heuristic’s computational efficiency rests on solving the sub-problems independently and on its novel handling of the master problem. Conceptually, it reformulates the master problem to avoid the duality-gap. Technologically, it provides the first application of Leontief substitution flow problems in MSMIP and thereby shows that hypergraphs lend themselves to loosely coupled MSMIPs. Computational results demonstrate superior performance of the heuristic approach and also show how this heuristic still applies when the SCND problem is modeled as a SMIP where the restricted master problem is a shortest-path problem.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.subjectSupply Chain Network Designen_US
dc.subjectMultistage Mixed-integer Stochastic Programmingen_US
dc.subjectHypergraphsen_US
dc.titleSupply Chain Network Design Under Uncertain and Dynamic Demanden_US
dc.typeThesisen
thesis.degree.departmentIndustrial and Systems Engineeringen_US
thesis.degree.disciplineIndustrial Engineeringen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.levelDoctoralen_US
dc.contributor.committeeMemberChen, Jianeren_US
dc.contributor.committeeMemberHicks, Illya V.en_US
dc.contributor.committeeMemberNtaimo, Lewisen_US
dc.contributor.committeeMemberRobinson, Powellen_US
dc.type.genrethesisen_US
dc.type.materialtexten_US


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