Güzin Bayraksan Awarded NSF Grant
Stochastic programming aids in solving difficult problems with many unknown factors. It does so by relying on probability distributions to mathematically represent and predict uncertain events. However, probabilities of possible outcomes are rarely known in real life. Distributionally robust optimization aims to obtain solutions in the presence of such distributional uncertainties. There are a variety of ways to form distributionally robust stochastic programs. However, which type of model to use for which type of data, system, or decision maker is not well understood. This award supports research to have a deeper understanding of this fundamental question and to explore multi-period uncertainties. The project considers long-term water resources management problems that take various sources of input including climate data, hydrological simulations, expert opinions, and so forth. The results, if successful, will yield improved water management, benefitting the U.S. society and economy. The research findings will be incorporated into educational materials on stochastic optimization. The project will therefore contribute to educating students. The water application will be used to demonstrate the societal impact of our field and to attract women to engineering.