Students will have a scientifically sound and practical understanding of linear, non-linear, stochastic and dynamic optimization. Based on this, they are able to formalize and model practical technical-economic decision problems. They have the ability to independently and creatively develop adequate solution procedures for given problems. Students have the necessary awareness and methodological competence to analyze, solve and interpret optimization problems encountered in practice. Working on bonus tasks in small groups provides students with the opportunity to broaden their social competence.
- Graph theory basics
- Path and flow problems
- MPM network planning technique
- Modeling of business and technical problems
- Linear programming
- Simplex method
- Duality principle and economic interpretation
- Basics of computer-aided linear optimization
- Integer programming
- Nonlinear programming
- Dynamic programming
- Stochastic simulation
- Bazaraa, M. S. (1993): Nonlinear Programming
- Domschke, W. and A. Drexl (2007). Einführung in Operations Research. 7th ed., Springer.
- Hillier, F. S., Lieberman, G. J. (2004): Introduction to Operations Research
- Kolonko, M (2008): Stochastische Simulation: Grundlagen, Algorithmen und Anwendungen
- Neumann, K. and M. Morlock (2002). Operations Research. 2nd ed. Munich: Carl Hanser.
- Werners, B. (2013): Grundlagen des Operations Research
- Winston, W. L. (2004). Operations Research. 4th ed. Duxbury Press.