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.
Click
Here for a listing of recent
papers on Stochastic & Dynamic Optimization.
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