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The here presented thesis deals with optimization problems where the underlying problem data are subject to uncertainty. Sources of data uncertainty in practical problems are manifold, and so are the ways to model uncertainty in a mathematical programming context. The position taken in this thesis is that the underlying problem is a linear or mixedinteger program where some part of the problem data, e.g., the constraint matrix, is described by a set of possible matrices instead of a single one. There are two opposite viewpoints on this: The optimist assumes that he can influence the uncertainty and, thus, can choose a constraint matrix along with values for the variables of the underlying problem. The pessimist, however, assumes that he has to take a decision without having this possibility to choose and, therefore, assumes the worst case. The former viewpoint is expressed by a so called generalized mixed-integer program, the latter by a so called robust mixed-integer program.