The aim of the course is to familiarize students with formulating and solving optimization problems with uncertainty. Students get acquainted with exact solutions methods as well as heuristics.
At the end of the course a student is able to formulate, implement and solve:
- basic stochastic optimization and learning models, e.g., related to Approximate Dynamic Programming and Optimal Learning;
- a stochastic programming model for a given problem context;
- stochastic models for strategic capacity planning and performance analyses.
Many problems from the field of operations management can be modeled and solved as a combinatorial optimization problem. However, in practice, many of such problems involve some stochasticity. Solving such a problem while accounting for the uncertainty is often extremely hard, and an approximate solution might be favored. In this course we deal with problems that involve uncertainty and that arise from real world applications. We focus on how to formulate such problems, and how to solve them using exact and approximation methods and algorithms.|