DR. Erhan Kutanoglu
Operations Research and Industrial Engineering
Cockrell School of Engineering
The University of Texas at Austin
Date: Friday, Nov 15, 2019
Time: 1 - 1:50 pm
Abstract: Hurricanes and similar severe weather events cause devastation to human life and critical infrastructures. In 2017, 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 on the critical infrastructure, and of enhancing its resilience by improving preparedness planning. In this talk, we present a comprehensive framework for a large-scale patient evacuation problem when an area faces a forecasted severe weather event. We integrate a hurricane impact prediction scheme and a 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 present preliminary computational results from our research using the real-world data from the Southeast Texas region. We finally tie this patient evacuation work to our larger integrated predictive-prescriptive analytics project on critical infrastructure resilience.
Biography: Erhan Kutanoglu is an associate professor of ORIE in the Cockrell School of Engineering at the UT 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.