Data-driven multi-stage stochastic programming models for integrated hurricane relief logistics and evacuation problem

Abstract Hurricanes are among the deadliest annual disasters in the United States, posing significant challenges for disaster response and evacuation planning. Forecasts from the National Hurricane Center are essential for guiding evacuation and logistics decisions, but these forecasts are subject to uncertainty, complicating the modeling of evacuation and relief logistics. This paper proposes a data-driven multi-stage stochastic programming (MSSP) model for integrated hurricane relief and logistics evacuation planning under forecast uncertainty, aimed at improving out-of-sample (OOS) performance. The framework captures the Markovian dynamics of hurricane track and intensity by leveraging historical forecast errors and kernel regression for conditional distribution estimation. We formulate a data-driven MSSP model within a distributionally robust optimization framework, incorporating historical forecast errors to improve decision-making under uncertainty. We present an approach for utilizing Markov chain (MC) discretization to reduce computational complexity with a trade-off in asymptotic optimality. Through extensive experiments based on a case study of Hurricane Florence, we demonstrate that the data-driven approach improves OOS performance in worst-case scenarios compared to the MSSP model with the nominal MC. Finally, we provide insights into how the data-driven model’s performance varies with key problem parameters.