Decision Modeling and Optimization

The following topics are taught in this course:

• Linear optimization (graphical method, Simplex method, sensitivity analysis and duality)
• Integer programming (Branch and Bound)
• Network models (transport, transshipment, travelling salesman, shortest path and similar models)
• Non-linear optimization (with/without constraints, gradient descent method, KKT conditions)
• Decision tree model
• Introduction to dynamic optimization and reinforcement learning.

Knowledge: The student

• learns about the variety of practical decision problems that can be described by means of quantitative models.
• receives knowledge about necessary components and properties of quantitative decision models.
• obtains knowledge about solution methods or algorithms that are useful to find solutions to decision problems and models.

Skills: The student will be enabled

• to translate practical decision problems into quantitative models
• to analyze the properties of decision models
• to apply appropriate methods for finding solutions to decision models,
• to implement decision models with software (Excel, Python),
• to transform complex models into models that are better accessible by solver software.

General Competence:

The students learns how to apply their knowledge and skills in different practical situations. They will be encouraged to reflect about advantages, shortcomings and further reaching implications of their models and solutions.

Lectures (physical or digital), videos, written exercises and data exercises.