With closer inspection, almost any practical optimization problem is affected by uncertainty. Growing interconnection of economic activity and liberalization of markets for energy and raw materials are striking examples for the increasing role of uncertainty in practical optimization. Stochastic programming offers a wide spectrum of mathematical models for coping with uncertainty. Structural analysis of these models and, on top of that, the design of practicable solution procedures are in the core of current research and often require completely new mathematical approaches. This leads to a fruitful interplay of fairly different mathematical disciplines such as analysis, discrete mathematics, and probability theory. This way, algorithms and software have emerged in recent years, which enable the successful solution of stochastic programs from practice whose complexity is ever growing.