ORIE’s Center for Engineering and Decision Analytics (CEDA) held its Annual Meeting and Research Showcase on August 31, 2020. CEDA sponsors in attendance included GE Aviation, IBM, MD Anderson Cancer Center, Sandia National Laboratories, Chevron, LMI, and Visa. CEDA Director, Prof. Eric Bickel, discussed CEDA’s research plan and launched a strategic planning activity for the ORIE program.

Five students presented their CEDA-related research. Emily Conley described her work on transportation analytics. Colin Small presented his analysis of COVID-19 forecasting models. Jeb Baum discussed the results of his analytics work with MD Anderson. Ryan Farell presented his research that integrates machine learning, geostatistics, and decision analysis. Finally, Batu Calci presented his model of the US natural gas / LNG network.

If you would like to learn more about CEDA, please contact Prof. Bickel.

We are please to report that ORIE Director, Eric Bickel, has been promoted to the rank of full professor. Eric joined ORIE in 2008 as an assistant professor. Prior to joining ORIE at The University of Texas at Austin, he spent four years as an assistant professor at Texas A&M University. Prior to this, Eric was a strategy consultant in Houston. Eric is thrilled with this promotion and looks forward to focusing more of his energies on the ORIE program and its students.

Over the last five months, Prof. Eric Bickel and PhD student Colin Small have been busy tracking and analyzing the forecasts provided by a range of COVID-19 forecasting models. They have compared these forecasts to a simple, one-parameter, forecasting model that they developed in mid-March. Surprisingly, they have found that their simple model performs as well as the best COVID-19 models and much better than many others. If you would like to learn more, please follow the link to a TexTalk they gave describing their work.

Optimizing ambulance dispatch and routing is among the most efficient ways for Emergency Medical Services (EMS) to save more lives at almost no extra cost. However, existing algorithms are designed for normal demand and often do not model hospital overflows. They are unable to adapt to disasters such as the COVID-19 pandemic, where case clusters emerge and hospitals rapidly reach capacity for weeks. Decisions optimized for normal times can suddenly become very inefficient, significantly delaying care. For example, in normal times, ambulances should take patients to the nearest hospital. However, in a pandemic, this strategy quickly inundates hospitals at the epicenters. A better system would use real-time data on incidents types and locations and hospitals’ capacities to optimally distribute patients across multiple equipped hospitals.

Associate Professor Dragan Djurdjanovic has been elected into the status of Associate Member of the International Academy for Manufacturing Research.