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Stochastic and Dynamic Optimization

Real-world problems almost invariably involve uncertainties. Stochastic optimization models capture these uncertainties by incorporating random variables and probabilistic statements into their deterministic counterparts. Some of this uncertainty might evolve over time and decisions can be made in stages as uncertainty is revealed. These types of optimization models have been successfully applied to a wide range of problems arising in finance, energy, transportation, telecommunications, and supply-chain management, among other areas. The UA faculty has been investigating the theoretical properties of, designing algorithms for and working on several different applications for this class of optimization problems.

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