Mathematical optimization is often used as a tool for process planning. Although the input data is usually affected by measurement errors and a high degree of uncertainty, most standard approaches in combinatorial optimization and integer programming assume the input data to be deterministic. One method to deal with uncertainty is robust optimization. In this lecture, we study robust optimization methods and frameworks which can be used in order to robustify classical optimization problems for both, theoretical and practical applications.
The course will only take place during the second half of the semester. M.Sc. BWL, M.Sc. WiWi, M.Sc. WiIng receive 5 CP for 4 hours lecture and 4 hours exercise a week, for B.Sc. Math the lecture and exercise are just a part of "Optimierung unter Unsicherheiten".
For further information we refer to Campus:
All students attend the same lecture.
Exercise We+Fr for Wiwi, BWL, WiIng
Exercise Th for Math, Computer Science