A multi-horizon stochastic programming model for long-term disaster housing logistics planning

Direct disaster housing plays a critical role in mitigating the social costs of displacement and alleviating the suffering of disaster victims. However, effective disaster housing logistics planning remains a significant challenge, as demonstrated by past major disasters. In this paper, we propose a data-driven decision-support framework that integrates disaster housing demand estimation into long-term disaster housing logistics planning through a multi-horizon stochastic programming (MHSP) model. The MHSP model explicitly accounts for both long-term and short-term demand uncertainty. We develop new solution methods tailored for the MHSP model and validate the proposed framework through numerical experiments that demonstrate its advantages over conventional approaches. Our results highlight the value of incorporating short-term uncertainty into long-term logistics planning, providing insights for policymakers to enhance disaster housing preparedness and response.