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dc.contributor.advisorErera, Alan L.
dc.contributor.advisorWhite III, Chelsea C.
dc.contributor.authorMalladi, Satya Sarvani
dc.date.accessioned2018-08-20T15:35:47Z
dc.date.available2018-08-20T15:35:47Z
dc.date.created2018-08
dc.date.issued2018-05-18
dc.date.submittedAugust 2018
dc.identifier.urihttp://hdl.handle.net/1853/60237
dc.description.abstractIn this dissertation, we examine resource mobility in a supply chain that attempts to satisfy geographically distributed demand through resource sharing, where the resources can be inventory and manufacturing capacity. Our objective is to examine how resource mobility, coupled with data-driven analytics, can result in supply chains that without customer service level reduction blend the advantages of distributed production-inventory systems (e.g., fast fulfillment) and centralized systems (e.g., economies of scale, less total buffer inventory, and reduced capital expenditures). We present efficient and effective solution methods for logistics management of multi-location production-inventory systems with transportable production capacity. We present a novel, generalized representation of demand uncertainty and propose data-driven responses to the manage a single location inventory system under such demands.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectDynamic capacity logistics
dc.subjectMobile, transportable production
dc.subjectDemand learning
dc.subjectJoint capacity
dc.subjectInventory control
dc.titleData-driven reconfigurable supply chain design and inventory control
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentIndustrial and Systems Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberToriello, Alejandro
dc.contributor.committeeMemberGoldberg, David A.
dc.contributor.committeeMemberZhou, Enlu
dc.contributor.committeeMemberGhosh, Soumen
dc.date.updated2018-08-20T15:35:47Z


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