Stochastic Programming Approaches to Logistics and Resource Optimization in Hurricane, Earthquake, and Flood Disaster Response

Abstract

Efficient disaster response planning is critical for minimizing human loss and economic impact during natural disasters such as hurricanes, earthquakes, and floods. However, inherent uncertainties—such as disaster intensity, affected population distribution, infrastructure damage, and environmental factors—complicate effective response strategies. This paper explores advanced stochastic programming methodologies as powerful mathematical tools for managing these uncertainties in disaster logistics and resource optimization. We synthesize and extend recent approaches, including multi-stage adaptive frameworks for hurricane response, two-stage optimization for evacuation routing, integrated facility location and casualty management in earthquake scenarios, and risk-based reservoir management during flooding events. The proposed unified stochastic optimization framework leverages rigorous mathematical modeling and operations research principles to enhance decision-making adaptability, optimize resource allocation, and improve overall emergency response effectiveness. Computational insights, real-world applicability, and practical recommendations are discussed, providing clear pathways for implementing these methods into actionable disaster preparedness and response policies