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.

The basic contents and concepts are presented by the head of the LVA and illustrated with the help of examples and case studies, and thus practiced by the students.

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

Written exam at the end of the semester.