After successful completion of the course, students are able to formulate important classes of stochastic optimization problems and to solve simple problem instances using software. In addition, they can describe and explain important theoretical properties of the model classes examined. The students are able to discuss and justify their results in the group.

risk averse optimization

Two stage and multistage recourse problems

Nonanticipativity, value of information and value of the stochastic solution

probabilistic constraints

(L-shaped method and further numerical approaches)

lecture

group work

implemented optimization model + oral exam