Positioning Disaster Relief Teams Given Dynamic Service Demand: A Hybrid Agent-Based and Spatial Optimization Approach

摘要

During extreme events, like hurricanes, it is essential to position relief services for non-evacuees throughout the affected region in optimal locations. While previous research has explored a variety of spatial optimization models to accomplish such a task, most work assumes that the population with demand is relatively static. However, this assumption neglects to account for potential feedbacks in the relief distribution system. For example, a population's behavior can both affect and be affected by the placement of relief services, resulting in a dynamic spatial distribution of demand. This article presents a hybrid modeling approach that utilizes GIS data with agent-based and spatial optimization models to position emergency relief teams in Bay County, Florida during a hurricane event. Non-evacuating household agents choose to remain at home or seek shelter, while relief team agents are periodically repositioned to account for changes in the spatial distribution of household agents. A total of 220 simulations are run to explore a variety of scenarios. Results show different repositioning timing strategies and the magnitude of the feedback effect drastically changes the level of access households have to relief teams. Ultimately, this work demonstrates the importance of accounting for behavioral and spatial dynamics in disaster relief distribution systems.