Many real life system are subject to uncertainty and should therefore be modelled with stochastic models. In this course we focus on the theory and the application of three different classes of stochastic models: Discrete Time Markov Chains, Continuous Time Markov Chains, and Markov Decision Processes. The students should gain knowledge about these models such that they are able to construct these models and apply them to solve real life problems. For illustration we use among others models of inventory systems, manufacturing systems, maintenance systems and queuing systems. We show how formulas for performance measures can be derived and how they can be computed. Further, the students learn numerical methods to obtain solutions. Additionally, we discuss methods to derive structural results and to obtain optimal policies.
Online-Course: 15.3.2021 – 1.4.2021
The course will be offered in electronic form. Participants get screencasts and exercises to study the different topics themselves. Additionally, virtual meetings are organized during the course to discuss the different topics and to support the participants
Face to Face time:
The course starts with an introductory session on 15.3.2021 10.00-11.00
Regular virtual meetings take place at the following dates and times
During the last meeting each student has to give a short presentation 1.4.2021, 10.00-18.00
Prof. Dr. Gudrun P Kiesmüller, TUM Campus Heilbronn, TU München