In the first six weeks, we will discuss the stochastic programming methodology. We will cover two-stage models, L-shaped method, multi-stage models, decomposition methods, and chance-constrained models. In the next three weeks, we will discuss stochastic dynamic programming methodology. We will cover finite horizon models, backward induction and monotone optimal policy. In the last three weeks, we will discuss the robust optimization methodology. We will cover uncertainty sets, two-stage models and multi-stage models.
If you need an outline from an
earlier
semester, please use this form to contact us. Outlines for current and
future
courses will be posted here on the website as they become available.
