A stochastic programming approach for floods emergency logistics |
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| Institution: | 1. Faculty of Engineering, Universidad Diego Portales, Chile;2. PhD student at Research Centre for Operations Management, University of Leuven, Belgium;3. Transport Engineering Consultant at Steer Davies Gleave, Chile;1. Research Center on Modern Logistics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;2. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;3. Department of Systems & Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA;1. School of Economics and Management, Beihang University, Beijing 100191, China;2. Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beijing 100191, China;3. Department of Public Order, National Police University of China, Shenyang, Liaoning 110854, China;4. Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089-0193, United States |
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| Abstract: | This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles’ availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events. |
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| Keywords: | Emergency logistics Stochastic programming Sample average approximation Chance constrained programming |
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