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The main idea of the presented new approach is to join a discrete event simulation (DES) and mathematical programming techniques (i.e. mixed integer programming, MIP) for optimization of complex manufacturing processes. Thereby, a DES model allows a detailed problem description. For a target oriented optimization several capacity allocation problems are solved by a MIP solver, reducing the degrees of freedom in the DES model. As an example a typical parallel machine scheduling problem arising in semiconductor industry was chosen. Different process constraints like machine dedications, setups, auxiliary resources and processing time dependences are discussed - advantages and disadvantages of simulation-based and exact scheduling approaches are drafted. The investigated optimization goals comprise the reduction of total tardiness and setups efforts as well as a balanced machine utilization. Based on real manufacturing data of a wafer test area this approach is evaluated.