After successful completion of the course, students are able to
Decision analysis, model-based decision support with focus on mathematical models; modelling process; simulation versus mathematical models, optimisation models; measuring productivity and efficiency (Data Envelopment Analysis); waiting line models; network planning and graph theory models; inter-temporal optimisation; modelling languages (GAMS).
The contents are presented in lectures and developed in accompanying exercises by students. In addition, case studies as well as small projects will be elaborated independently or in groups.
The theoretical background is tested by two to three written tests. In order to take on the skills, students develop examples and case studies both in class and at home.
It is recommended that students calculate with matrices, discuss elementary functions, apply Bayes' theorem, explain conditional probabilities, experiment with algorithms, and work on and use programming codes.
