This thesis studies two-stage stochastic optimization methods for supply prepositioning for hurricane relief logistics. The first stage determines where to preposition supplies and how much to preposition at a location. The second stage decides the amount of supplies distributed from supply centers to demand centers. The methods proposed are (I) a method to minimize the expected total cost (II) a method to minimize the variance of the total cost that accounts for the uncertainties of parameters of the expected cost model. For method II, a Bayesian model and a robust stochastic programming solution approach are proposed. In this approach the cost function parameters are assumed to be uncertain random variables. We propose a Mixed Integer Programming model, which can be solved efficiently using linear and nonlinear programming solvers. The resultslinear and nonlinear integer programming problems are obtained solved using CPLEX and FILMINT solvers, respectively. A computational case study comprised of real-world hurricane scenarios is conducted to illustrate how the proposed methods work on a practical problem. A buffer zone is specified in order to be sent of the commodities to a certain distance. Estimation of hurricane landfall probabilities and the effect of cost uncertainty on prepositioning decisions is considered. We propose a Mixed Integer Programming model, which can be solved efficiently using a linear and nonlinear programming solver. The results are obtained using CPLEX and FILMINT.
|Commitee:||Ozguven, Eren E., Park, Chiwoo, Wang, Hui|
|School:||The Florida State University|
|Department:||Industrial and Manufacturing Engineering|
|School Location:||United States -- Florida|
|Source:||MAI 58/01M(E), Masters Abstracts International|
|Keywords:||Bayesian analysis, Disaster relief, Inventory management, Optimization, Stochastic programming|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be