Optimizing cross-dock operations under uncertainty
dc.contributor.advisor | Waller, S. Travis | en |
dc.contributor.committeeMember | Bhat, Chandra R. | en |
dc.contributor.committeeMember | Machemehl, Randy B. | en |
dc.contributor.committeeMember | Morton, David P. | en |
dc.contributor.committeeMember | Zhang, Zhanmin | en |
dc.creator | Sathasivan, Kanthimathi | en |
dc.date.accessioned | 2012-01-30T20:09:30Z | en |
dc.date.accessioned | 2017-05-11T22:23:56Z | |
dc.date.available | 2012-01-30T20:09:30Z | en |
dc.date.available | 2017-05-11T22:23:56Z | |
dc.date.issued | 2011-12 | en |
dc.date.submitted | December 2011 | en |
dc.date.updated | 2012-01-30T20:09:39Z | en |
dc.description | text | en |
dc.description.abstract | Cross-docking is an important transportation logistics strategy in supply chain management which reduces transportation costs, inventory holding costs, order-picking costs and response time. Careful planning is needed for successful cross-dock operations. Uncertainty in cross-dock problems is inevitable and needs to be addressed. Almost all research in the cross-dock area assumes determinism. This dissertation considers uncertainty in cross-dock problems and optimizes these problems under uncertainty. We consider uncertainty in processing times, using scenario-based and protection-based robust approaches. Using a heuristic method, we find a lower and upper bound and combine that with a meta-heuristic method to solve the problem. Also, we consider problems in two different industries (Goodwill and H-E-B) and address the uncertainties that happen frequently in their operations. The scenario-based robust optimization model for the unloading problem using a min max objective is presented with examples. A surrogate heuristic procedure is used to find a robust solution. Next, a two-space genetic algorithm, a meta-heuristic procedure, is applied to the unloading problem using the bounds obtained by the heuristic procedure. The results are closer to the optimal solution than those obtained by the two-space genetic algorithm without bounds. When compared with the regular genetic algorithm with bounds, the two-space algorithm performs well. The protection-based approach considers a limit on the number of coefficients allowed to change with data uncertainty, protecting against the degree of conservatism. The management of trucks and reduction of overtime pay in the cross-dock operations of Goodwill is addressed through two models and uncertainty is applied to those models. A combined cross-dock operations model together with demand is formulated and the uncertainties are discussed for H-E-B operations. This dissertation does not address the recycling operation within the cross-dock of Goodwill, or the uncertainty in H-E-B data. | en |
dc.description.department | Civil, Architectural, and Environmental Engineering | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.slug | 2152/ETD-UT-2011-12-4589 | en |
dc.identifier.uri | http://hdl.handle.net/2152/ETD-UT-2011-12-4589 | en |
dc.language.iso | eng | en |
dc.subject | Cross-docking | en |
dc.subject | Uncertainty | en |
dc.subject | Robust optimization | en |
dc.title | Optimizing cross-dock operations under uncertainty | en |
dc.type.genre | thesis | en |