Holger Marten Berthold

• Optimization under uncertainty is one field of mathematics which is strongly inspired by real world problems. To handle uncertainties several models have arisen. One of these is the probust model where a combination of probabilistic and worst-case uncertainty is considered. So far, just problem instances with a special structure can be dealt with. In this thesis, we introduce solving techniques applicable for any probust optimization problem. On the one hand, we create upper bounds for the solution value by solving a sequence of chance constrained optimization problems. These bounds are based on discretization schemes which are inspired by semi-infinite optimization. On the other hand, we create lower bounds by solving a sequence of set-approximation problems. Here, we substitute the original event set by an appropriate family of sets. We examine the performance of the corresponding algorithms on simple packing problems where we can provide the probust solution analytically. Afterwards, we solve a water reservoir and a distillation problem and compare the probust solutions with solutions arising from other uncertainty models.