- Learning Outcome 2 (LO2): Explain the quantitative modeling and solution approaches covered in the chosen topic area.
- Learning Outcome 3 (LO3): Select, apply and adapt appropriate algorithmic approaches.
- Learning Outcome 4 (LO4): Formulate planning and decision problems (both those covered and new variants) in the subject area in a structured mathematical way.
- Learning Outcome 5 (LO5): Implement the models covered, and partially also apply them to data sets and evaluate and interpret the obtained results.
|
Learning Outcome 1 (LO1): Recall the various mixed integer programming approaches and the respective exact and heuristic optimization alogrithms for strategic and operational planning and decision problems.
Depending on the subject area, different problems, models, solution approaches and concepts essential to the topic area are taught and their application to practical problems is discussed. Here are two example subject areas:
Subject area “Transportation Logistics”: Students are familiar with modeling and solution approaches for long-term tactical planning problems in goods distribution planning and short-term operational planning (traveling salesman problem, vehicle routing problem and variants thereof).
Subject area “Multi-Objective Optimization”: Students know modelling and solution approaches for planning and decision-making problems with multiple objectives, e.g. location planning taking into account costs and CO2 emissions or distribution planning in a disaster relief context.
|
