Approaches for Locating and Staffing FEMA's Disaster Recovery Centers.

Disaster response is a challenging environment for data‐driven decision support, because of its rarity, uniqueness, and high stakes. This article develops and demonstrates practical decision support approaches for an important problem faced by the U.S. Federal Emergency Management Agency (FEMA), identifies challenges and successes during their partial implementation, and explores how such approaches can encourage the adoption of data‐driven decision‐making in crisis situations. Specifically, this article develops approaches for locating and staffing temporary Disaster Recovery Centers (DRCs), which provide in‐person service to disaster‐affected communities. Working with FEMA, we developed two models that aim to improve service to survivors and minimize costs. One model fits easily into FEMA's current decision‐making process, while the other further improves service by challenging some extant assumptions. By testing the models using data from three past disasters, we find that this decision support can result in cost savings of 75% on average by eliminating unnecessary DRCs and over‐staffing and, at the same time, maintain or reduce the maximum travel time required for the disaster‐affected population to access DRCs. Aspects of the models have already been used during disaster operations. FEMA's experience highlights the potential for data‐ and model‐driven analyses to improve resource allocation and demonstrates an approach to improve organizational decisions by developing models that either align with or challenge the decision‐making culture. [ABSTRACT FROM AUTHOR]