Monte Carlo simulation is one of the most successful techniques, not only in operations research and performance evaluation, but in science in general. One reason for this extraordinary success is its flexibility. In contrast, most queueing models are rather specialized. In this talk, we suggest methods to make queueing theory more flexible. In particular, we suggest an event-based approach, which provides great flexibility for the modeller. We also show how to convert such event- based models into Markov chains, which can then be solved by classical numerical methods. The suggested method is particularly suited for small models, where its execution times are much lower than Monte-Carlo simulation. For larger problems, the curse of dimensionality takes over, and the execution times based on classical numerical methods increase exponentially. This means that for complex models, simulation finds numerical solutions with less computer time than classical numerical methods.