A simulation-based data-driven analysis for improving collaboration between food bank facilities before and after natural disasters
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Food banks are non-profit organizations that gather and distribute supplies to agencies serving people in need. Food banks obtain donations (i.e., supplies) from the public and public and private organizations. Catastrophic events like hurricane Harvey leads the way to complexities for serving people in need. Food banks act as food storage and distribution depots for smaller front-line agencies and usually do not give out food directly to people struggling with hunger. The demand for supplies needed by supporting agencies is very dynamic and challenging to predict. The demand for supplies required to collaborating agencies is very dynamic, especially after natural disasters when food banks become significant players in disaster relief efforts. Therefore, planning for the operation of food banks under both normal and disaster relief conditions is a challenging problem. This research aims to develop data-driven models for improving the operation of a network of food bank facilities in charge of providing relief after natural disasters. This research provides a methodology for achieving the defined objective by developing simulation and optimization models for decision-making purposes.
The first part of this thesis is to develop a discrete-event simulation model of a network of food banks to investigate the impact of multiple disaster relief operational policies (i.e., supply prepositioning, distribution center assignment). The model simulates the flow of donations at three food bank facilities and the demand for supplies of 55 demand locations before and after a natural disaster. The simulation model is validated with the real-time data collected at the food bank facility. Discrete-event simulation model experiments are conducted from the results of the stochastic model developed in the second part of the thesis. The value of the simulation model is demonstrated through the analysis of 21 scenarios, five optimization-based demand distribution policies, and six performance measures: unmet demand, total demand met, daily number of trips, delivery cycle time, demand fulfilment rate and the order fulfilment rate. The results of the simulation model show that there is a 20% increase in the overall demand fulfillment rate if food banks operate as an integrated network with supply prepositioning and demand splitting between operating facilities.
The second part of this thesis is to develop optimization-based decision-making policies for pre-positioning disaster relief supplies considering the transportation limitations of network food bank facilities and the uncertainty in demand. The stochastic programming model determines the best distribution decisions considering supply chain disruptions after hurricane events. The first stage models the pre-positioning of supplies between food banks, while the second stage provides recursive actions for supplies prepositioning and models supplies distribution to the demand nodes under different scenarios. The developed stochastic model will identify the least-cost strategy associated with pre-positioning existing supplies that will satisfy the demand needs after a natural disaster by considering various parameters like storage cost, transportation cost, truck availability, and docks availability.
CitationKothamasu, M. (2021). A simulation-based data-driven analysis for improving collaboration between food bank facilities before and after natural disasters (Unpublished thesis). Texas State University, San Marcos, Texas.