Logistics planning for direct temporary disaster housing assistance under demand uncertainty

In this paper, we propose and study a framework for disaster housing logistics planning under demand uncertainty. Specifically, we utilize a two-stage chance-constrained stochastic programming model to achieve the balance between logistics operational cost and demand fulfillment especially towards extreme disaster scenarios. To do so, we incorporate two operational modalities, one for the ordinary modality and the other for the emergency modality, and the emergency modality is only allowed to be activated for a certain percentage of scenarios that is specified by the decision maker among all scenarios. The set of scenarios is generated according to a spatial regression model for characterizing the disaster housing demand based on a selected number of independent variables related to both the hazard and socioeconomic factors, which is trained offline from historical data. We conduct a numerical experiment based on Hurricane Ian, and our numerical results show the effectiveness of the proposed approach compared to some standard benchmark approaches. We also highlight the managerial insights for disaster housing logistics planning gained through this numerical experiment.