Time-Phased Relief Supply Network Planning for Foreseen Disasters

We consider emergency preparedness for foreseen disasters such as hurricanes and flooding. Proper preparation and planning for timely relief supply in a cost-effective manner can be crucial determinants of how quickly recoveries occur and with the least suffering of the affected populations. Assuming a host of shelter locations aggregated locally, we are interested in determining relief supply locations (distribution centers [DCs]) and routing of supply from the capacitated DCs to the shelters on an underlying time-phased network for cost-effective timely delivery. We present a mixed integer optimization model to address this problem under the assumption of covering the worst case demand at the shelters. To solve our model, we develop an efficient Benders decomposition–based algorithm that handles the challenges of obtaining optimality cuts via master problem solution modifications and various surrogate constraints among other enhancement techniques. We test the performance of the enhancement techniques on an extensive randomly generated test data set to identify the most effective approach. We finally use our model and the solution algorithm on an actual case study in southern Texas data incorporated and managed by a geographical information system to examine the impact of various input parameters on the design and tactical operation of the relief networks as well as for further model verification and validation.

Funding: This research was supported by the National Science Foundation [Grant CMMI-2114102].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0217.