After successful completion of the course, students are able to

• model (and apply mathematical methods) in typical operational issues such as production planning, supply chains and inventory planning, performance measurement, route planning, decision theory, etc.
• distinguish between simulation models and mathematical models (descriptive versus prescriptive models)
• discuss and apply the fundamentals of graph theory and to prove selected theorems of graph theory (selected on the basis of exemplary proof approaches)
• to explain and apply important algorithms of graph theory
• model stochastic, multi-stage problem formulations in Markov chains
• calculate statistical equilibria of irreducible ergodic markov chains and thus to weigh alternative decisions or anticipated market shares
• choose adequate queuing models and calculate the characteristic measures (such as expected length of the queue) of a queuing system and use it as decision support
• sketch basic concepts, models and methods of game theory
• explain the concepture of the Column Generation Method for large scale LPs (based on the case studies).

Model-based decision-support, abstraction and modeling, juxtaposition of descriptive and prescriptive models, OR case studies, efficiency and productivity measurement, introduction to queuing theory, and brief portrait of graph theory and game theory.

In the exercises, examples that explain, enhance, deepen and expand the lecture are treated.

First meeting on March 4, 2020, 1:10 p.m. FH HS 4

Students realize explanatory, illustrative and in-depth examples partly under guidance and partly independently and present them. The preparation and presentation of the examples form the basis of the assessment, short tests complement the evaluation of the achievement of the learning outcomes.