Disaster response involves the planning, coordination, and distribution of supplies in an effective manner to people in need. Recent natural hazards, such as hurricane Harvey, have exposed the complexities and challenges associated with those tasks. In this paper, a stochastic programming model is presented which considers prepositioning strategies among food bank facilities located in high-risks areas for hurricanes. The model considers the uncertainty associated with the impact of the hurricane at each facility in terms of the number of available supplies, donations received at the facility, and the expected demand for their service region. The first-stage decision attempts to minimize the number of people not receiving the needed supplies by prepositioning the existing supplies at each facility. Second-stage decisions maximize the system responsiveness by trying to satisfy the observed demand for the scenarios under consideration. The experiments consider scenarios in which one or two food bank facilities are shut down after the natural phenomenon and study the impact of prepositioning supplies.
We present the results of a simulation to assess the optimal design characteristics of rainwater harvesting systems to be used in a semi-arid region of the United States. The simulation leverages a stochastic, nonparametric rainfall generator based on 64-years of daily historical data. The assumption of nonstationarity of rainfall is also thoroughly investigated for this paper. Of specific interest to this simulation was the estimate of roof capture space and cistern capacity required for a 100% reliable system capable of supporting family sizes of two or three for a 30-year time horizon. Considerations included rainfall supply, system capture efficiency, household occupancy, as well as individual demand variation. The optimal design characteristics in terms of roof surface area and cistern volume necessary for 100% reliability are presented using two response surface plots and separate multiple regression modeling based on expected occupancy.
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