Course content

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.

Most of the models will be implemented by means of Excel spreadsheets and Excel solver.

Learning outcome

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,
• to transform complex models into models that are better accessible by solver software.

General Competence:

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

Learning methods and activities

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

Compulsory assignments

• To obligatoriske innleveringer

Specific conditions

Required previous knowledge

This course requires elementary courses in mathematics and managerial economics.

Course materials

The curriculum will be announced when the course commences.

Credit reductions