Vortragender: Dr. Bismark Singh
Efficiently solving large-scale optimization models for highly reliable systems
We study a collection of optimization models under uncertainty. First, we present new theoretical results for resource allocation optimization models, that ensure highly reliable systems by employing chance constraints. For the specific models we present, previous research considers stochastic programs that are only one-stage while we extend it to two-stages. Second, we summarize some of our works on allocating critical medical resources for the 2009 H1N1 and the current COVID-19 pandemics. We describe how our proposed systems are currently employed by the City of Austin, Texas. Third, we present a few of our miscellaneous works on the design of efficient algorithms for solving large-scale optimization models under uncertainty, such as a Benders decomposition algorithm for a stochastic $s – t$ cut problem. Our models employ actual data, from sources such as the US electrical grid or the ongoing pandemic. We conclude with other examples of our ongoing work on vehicle routing and the optimality conditions of convex optimization models.
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Meeting-ID: 666 6856 9417