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# Dissertation/Thesis Abstract

Supply Prepositioning for Disaster Management
by Baloglu, Aysegul, M.S., The Florida State University, 2018, 68; 10787949
Abstract (Summary)

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.

Indexing (document details)
 Advisor: Vanli, Arda 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 Source Type: DISSERTATION Subjects: Industrial engineering Keywords: Bayesian analysis, Disaster relief, Inventory management, Optimization, Stochastic programming Publication Number: 10787949 ISBN: 978-0-438-30747-6