Detailed information about the module 
Module title:
Stochastic Models
Module title (english):
Stochastic Models
Module number:
Master (UNI)
Course of study:
Organising faculty/Language Center:
Wirtschaftswissenschaftliche Fakultät
Instructors responsible:
Kuhn, Heinrich
Credit points (ECTS):
- learn to use and apply advanced methods of stochastic modeling and
- learn and understand to choose appropriate models and approaches, to apply them on production systems and for production-like systems in the service industry,
- develop the skills necessary to participate in scientific research in the field of operations management.
course content/topics:
Part 1: Fundamentals of Stochastic Modeling
1. Introduction, Applications, Modeling, Probability Theory
2. Random Distributions: Binomial, Geometric, Poisson, Weibull, Phase-Distributions
3. Discrete Markov-Chains
4. Markov-Chains in continuous time, Birth- and Death-Processes
Part 2: Queueing Theory
5. Little's Law, M/M/1-Model, M/M/c-Model
6. M/M/1/K-Model, M/M/1/K/K-Model, Mean-Value-Analysis: M/G/1-Model, G/G/1-Models
7. Open Queueing Systems: Jackson's Theorem, Systems with limited Buffers
8. Closed Queueing Systems: Mean-Value-Analysis (MVA), Convolution-Algorithms
formal requirements of admission:
recommended qualifications:
- Course contents of ABWL, Operations Management, and Management Science
- Knowledge of Basic Concepts in Statistics and Probability Theory
Lesson / exam language:
Lehr- und Lernformen/Lehrveranstaltungstypen:
requirements for the attainment of ECTS points:
Written exam at the end of semester
workload/distribution of ECTS points within the module:
24 h = Time of attendance lecture
24 h = Preparation and postprocessing lecture
24 h = Time of attendance tutorial
48 h = Preparation and postprocessing tutorial
30 h = Exam preparation
150 h = Total workload
calculation of module marks:
Final exam 100%
teaching/learning method:
- Presentation with slides
- homework
compatibility with other courses of study:
Turnus des Angebots:
Beteiligte Fachgebiete: