ISE Graduate Seminar
Integrated Flood Prediction and Stochastic Optimization: Logistics of Large-scale Patient Evacuation before Hurricanes
Operations Research and Industrial Engineering
Cockrell School of Engineering
The University of Texas at Austin
Hurricanes and similar severe weather events cause devastation to human life and critical infrastructures. In 2017, not only was Harvey the longest lasting hurricane to hit Texas, causing record levels of rainfall, but also the costliest at $130B, part of which was due to large-scale evacuation of patients from hospitals and nursing homes. In fact, SETRAC, the emergency medical operations agency for the Houston area, evacuated about 1500 patients from 25 medical facilities hours before Harvey made landfall. Experience shows the importance of accurately predicting the impacts of hurricanes and other heavy rainfall events on the critical infrastructure, and of enhancing its resilience by improving preparedness planning. Therefore, we propose a comprehensive modeling and methodological framework for a large-scale patient evacuation problem when an area is faced with a forecasted event such as a hurricane. In this work, we integrate an extensive hurricane impact prediction scheme and a scenario-based stochastic integer program for end-to-end patient evacuation decision support. The impact prediction scheme uses probabilistic hurricane forecasts, blending the uncertainties in hurricane intensity, direction, forward speed, and tide level. It further incorporates the state-of-the-art hydrology and hydraulics models (both for storm surge and rainfall) and the terrain of the affected region to generate flood mapping and hospital/nursing home impact scenarios. Taking the scenarios as input, the stochastic optimization model in turn makes decisions on staging area locations and positioning of emergency medical vehicles and integrated patient movements between sending and receiving facilities. We demonstrate the overall approach and present preliminary results from our research using the real-world data from the Southeast Texas region with a computational study. We finally tie this patient evacuation work to our larger integrated predictive-prescriptive analytics project on critical infrastructure resilience.
*Joint work with: K.Y. Kim (ORIE), W.Y. Wu (Geosciences), J. Hasenbein (ORIE), Z.L. Yang (Geosciences)
*Supported in part by the NSF Coastlines and People (CoPe) Program and the UT Austin Planet Texas 2050 Initiative
Erhan Kutanoglu is an associate professor of Operations Research and Industrial Engineering in the Cockrell School of Engineering at the University of Texas at Austin. His current research interests focus on integrated humanitarian logistics, particularly hurricane and other extreme weather event mitigation, preparedness and recovery decision making and optimization. His effort here is to combine predictive science-based models with prescriptive stochastic optimization models to develop an end-to-end understanding of uncertainty and optimized decision making in humanitarian logistics and disaster resilience, particularly for critical infrastructure such as healthcare and power grid. His other interests span manufacturing and service logistics optimization, including supply chain and network design, inventory management, transportation operations, and production planning and scheduling. He holds a PhD in Industrial Engineering from Lehigh University, is a recipient of NSF CAREER Award and IBM Faculty Award, and is an active member of INFORMS and IISE.
Friday, November 8 at 2:30pm to 3:30pm
John D. Tickle Engineering Building, 410
851 Neyland Dr, Knoxville, TN 37996