Models for Disaster Relief Shelter Location and Supply Routing

Models for Disaster Relief Shelter Location and Supply Routing

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Models for Disaster Relief Shelter Location and Supply Routing
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    This project focuses on the development of a natural disaster response planning model that determines where to locate points of distribution for relief supplies after a disaster occurs. Advance planning (selecting locations for points of distribution prior to the disaster) is complicated by the expectation that buildings and transportation infrastructure in the impact zone may experience damage. For example, highway bridges in affected areas are predicted to be non-functional after an earthquake. The response planning model developed in this project specifies how points of distribution should be chosen once the specific disaster scenario, and the damage caused, is known. The model relies on real-time information regarding actual damage to transportation infrastructure and locations of persons in need of assistance. Response time is critical in saving lives after a disaster, so an approximate solution approach is developed to obtain good solutions quickly. A case study motivated by a New Madrid Seismic Zone (NMSZ) catastrophic event is used to test the model. The case study region includes nineteen counties in Northeastern Arkansas that are most likely to sustain damage in such a scenario. Given a constraint on the total budget available to open and operate points of distribution, it is demonstrated that solutions obtained using the optimal offline approach are able to serve an average of 81% of total demand across test instances considered in a computational study. Solutions obtained using the approximate online approach are able to serve an average of 63% of total demand. A number of assumptions had to be made when populating the case study with data. The solutions presented here are intended simply to illustrate the model and solution approach. The quality of conclusions that can be based on the model and solutions will increase as higher-quality data becomes available for populating the model.
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