Our computations were done with a number of goals in mind. The main objective was to provide a schedule which satisfied all hard constraints and minimized the cost of medium and soft constraints, using real-life data sets for a large university. We also aimed to find an acceptable set of annealing parameters and move strategies for general timetabling problems of this kind, and to study the effect of using a preprocessor to provide the annealing program with a good starting point. Finally, we wanted to make a comparison of the performance of the three different cooling schedules, geometric cooling, adaptive cooling, and reheating based on cost.