Simulation of Discrete-Event Systems

Key Info

Basic Information

Professor Alexander Mertens, Professor Sven Tackenberg

Further Information



Team DES




The course is an introduction to simulation models of discrete-event systems. Discrete-event systems have a wide range of applications in computer-aided design, e.g., in the design of automated manufacturing systems or telecommunications systems. The course covers the methodological foundations of discrete-event systems, which originate in automata theory. Petri nets are introduced and analyzed in detail. The basic approaches are generalized to timed and stochastic models. The principles of Markov chains and queuing models are taught. In addition, other recent developments are presented. Finally, the main functions of discrete event simulators are explained based on the given theory.


Based on the theoretical contents for different practice-relevant topics taught in the lecture, the methods are applied and trained in weekly interactive exercises. Besides the understanding of basic models and approaches for the modeling and analysis of discrete-event systems, the focus is on the transferability of the contents to practical examples and the limitation of the expressiveness of simulations.

Teaching and Learning Method

Skill building is accomplished by applying the methodological principles taught in the lecture to simulation-based design and analysis in hands-on exercises. This weekly two-course format allows for the direct application of the methods and constructs taught.

Through the two-part structure, students develop both the skill to apply the available methods and mathematical models and to make informed decisions about which of the methods is most suitable for the issues at hand. In addition, the limitations in deriving conclusions from the simulated data due to technology and model-based pragmatism will be discussed, so that, after the successful completion of the course, students will be able to make sound context-specific assessments of the applicability and suitability of the methods and models.