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dc.contributor.advisorBarker, Kash
dc.contributor.authorBourgeois, Christopher
dc.date.accessioned2021-05-12T15:20:18Z
dc.date.available2021-05-12T15:20:18Z
dc.date.issued2021-05
dc.identifier.urihttps://hdl.handle.net/11244/329531
dc.description.abstractProduction facilities are critical components of the global economy and supply chain. Firms are forced to balance several competing objectives—from reducing operating costs by managing inventory to increasing profits by satisfying customer demand. The act of scheduling material through an interconnected production network is analytically challenging; therefore, stakeholders require statistical insights in order to strengthen decision making. This inventory scheduling application allows decision makers to monitor the status and impact of numerous production parameters, while aiming to mitigate the propagation of schedule risk through the system. This work extends the material planning framework from Soltanisehat et al. (2021) that (i) jointly represents work center and material relationships and (ii) integrates stochastic methods to assess potential risk. Using network flow optimization and Monte Carlo simulation, this extension accommodates three primary considerations: (i) safety stock and other inventory restrictions, (ii) multi-product order satisfaction, and (iii) costs derived from holding inventory and delaying orders. An illustrative example examines the impact of six experimental scenarios on order fulfillment, work center utilization, and material inventory and production.en_US
dc.languageen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectMonte Carlo simulationen_US
dc.subjectmaterial requirements planningen_US
dc.subjectproduction schedule risken_US
dc.subjectnetwork flow optimizationen_US
dc.titleInventory Scheduling Framework to Fulfill Multi-Product Orders within an Interconnected Production Networken_US
dc.contributor.committeeMemberGonzález, Andrés
dc.contributor.committeeMemberLi, Yifu
dc.date.manuscript2021-05
dc.thesis.degreeMaster of Scienceen_US
ou.groupGallogly College of Engineering::School of Industrial and Systems Engineeringen_US
shareok.orcid0000-0003-3219-2361en_US
shareok.nativefileaccessrestricteden_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International