In this book, we present novel methodologies for automatically generating online scheduling strategies with the help of real life workload data. The scheduling problem includes independent parallel jobs, multiple identical machines, and a complex scheduling objective. This objective is defined by the machine provider and considers different priorities of user groups. In order to allow a wide range of objective functions, we use a rule based scheduling strategy. There, a rule system classifies all possible scheduling states and assigns an appropriate scheduling strategy to the actual state. The rule bases are developed in three different ways. We evaluate our new scheduling strategies again on real workload data and provide a comprehensive comparison of the different approaches among each other. Further, we show the benefit of the developed rule based scheduling systems by comparing them to the main standard algorithms currently in use. To this end, we select several exemplary objective functions that prioritize some user groups over others.