New approaches to Robust Optimization
|Many practical problems suffer from inaccurate, missing, or unreliable input data. This is a severe problem, since even small changes can make an optimal solution completely useless for practice. Robust optimization approaches try to hedge against uncertain data. The goal is to find solutions which are good for all scenarios contained in some given uncertainty set.
In this talk we will give an overview about models on robust optimization including the "classical" concepts as well as the more recent concepts of light robustness and recovery robustness. We will then present a new generic approach which enables us to generate robust solutions if a solution procedure for the certain optimization problem is known. Properties and first numerical results will be presented.
We will also discuss the applicability of the various approaches in particular with respect to applications occurring in public transportation such as line planning, timetabling, and delay management.
|Wilco van den Heuvel|