The objective of this Faculty Early Career Development (CAREER) award is to develop optimization-based decision support models to assist emergency managers in planning, training, and operations for logistically complex emergency evacuations; specifically, the research will study both hospital and large-scale regional evacuations. These two evacuation problems differ from the logistical problems often encountered in industry because risk is an important consideration (evacuations are performed in response to a perceived threat) and they occur infrequently (at least for any particular hospital or region), making appropriate institutional knowledge scarce. Furthermore, hospital evacuations require decisions that are both medical and logistical in nature, while regional evacuations must consider social issues. To be successful, both evacuation problems require the integration of multiple processes and the coordination of a large number of resources in an efficient manner. While these problems are distinct, they can both be modeled using novel network formulations that are combinatorial in nature and involve high levels of uncertainty. Furthermore, solutions to both problems require robust, implementable strategies that consider risk. An operations research-based methodology will be used to study these evacuation problems.
If successful, this research will develop models that will improve planning, training, and operational management for these difficult evacuation problems. This research will be conducted in close collaboration with emergency managers and the other principle players. This research is important, and widely applicable, the hospital portion also applies to residential care facilities (which serve approximately 2.2 million people in the U.S.). Furthermore, as coastal populations grow, especially on the Gulf and Atlantic coasts, more sophisticated evacuation strategies will be required in response to seasonal hurricane evacuations, among other threats. The educational component of this award will seek to increase graduate school enrollment and diversity, and illustrate to practitioners and policy-makers the value of these decision support models.
