An Agent‐Based Model for Post‐Disaster Resource Allocation Considering Dynamic Demand

ABSTRACT

In the response phase of a disaster, effective and equitable resource allocation is critical for meeting the needs of affected populations and maintaining the social order in disaster‐stricken areas. However, dynamic demand driven by the mobility of disaster‐affected populations can substantially impact resource allocation outcomes. To consider this mobility in the resource allocation problem, in this paper, we employ an agent‐based model to simulate cross‐shelter movements and assess the performance of resource allocation strategies considering a gradual increase of total available resources over time. A novel resource allocation strategy is proposed, which leverages demand estimation through a machine learning (ML) method‐XGBoost. We then compare this approach against two conventional strategies: uniform distribution and historical data averaging. Through case studies using real‐world data of Typhoon Ragasa from Shenzhen in China and Lal‐lo in the Philippines, we demonstrate that our proposed strategy comprehensively outperforms the other two in terms of effectiveness, equity and stability. In conclusion, this ML‐based prediction approach offers valuable insights to support decision‐makers in the formulation of resource allocation plans.