Uncertainties abound in modeling real-world problems. Including uncertainty in an optimization model is now standard practice in industry, thanks to the development of both mathematical models and efficient software. In this course, we will discuss several classes of optimization problems that account for uncertainty in the problem data. The concepts of multistage problems, probabilistic constraints and risk measures will be used to derive the problem formulations of interest. We will also review algorithms that can be used to tackle stochastic programming problems, from both a theoretical and a practical perspective using recently developed packages.

References

•  M. Biel and M. Johansson, Efficient stochastic programming in Julia, INFORMS Journal of Computing (2022)
• J. R. Birge and F. Louveaux, Introduction to Stochastic Programming 2nd Edition, Springer (2011)
• A. Shapiro, D. Dentcheva and A. Ruszczynski, Lectures on Stochastic Programming, 3rd edition, SIAM (2021)