August 13, 2009, 11:15, Room GC.B3.424 (click here for the map)
In this work, we consider large-scale resource allocation problems under uncertainty. Inherent real-world uncertainty guarantees that deterministic optimal solutions are rarely, if ever, executed. By proactively modeling uncertainty, robust methods attempt to generate solutions that are less vulnerable to uncertainty. We will examine the application of robust methods such as the Bertsimas and Sim approach and Chance-Constrained Programming, which represent two different paradigms: worst-case and probabilistic modeling. We discuss differences in models and solutions generated by these approaches, and propose extensions to these models that overcome some crucial limitations.
Lavanya Marla is currently a PhD candidate at the Massachusetts Institute of Technology. She holds Masters degrees in Transportation and Operations Research from MIT, and a Bachelors degree in Civil Engineering from the Indian Institute of Technology, Madras.