Course content

The course material will partly be decided based on the background, experience and research interest of the students. Examples of themes that may be included are:

• Advanced linear programming theory
• Mixed integer linear programming formulations and reformulations
• Valid inequalities and cuts
• Decomposition methods for linear and nonlinear optimization
• Heuristics

The course is given every other year, next time Spring 2026.

Learning outcome

The position and function of the course within PhD studies in Operations Research:

This course is meant to be a common course for all PhD students at the Department of Industrial Economics and Technology Management (IØT) working with problems where knowledge about Operations Research is important. The course builds upon advanced operations research courses on master level and provides deepened knowledge about mathematical modeling and the formulation of optimization problems. It also provides knowledge about algorithms and solution methods.

The course will provide knowledge to understand advanced theory, models, methods, and concepts within optimization like:

• strengths and weaknesses with different ways of formulating technical and economical planning problems
• how different formulations and algorithms can be combined to efficient solution methods
• theory about linear programming, integer programming, and heuristics
• how to use commercial software to solve technical and economical planning problems
• knowledge about many different models and when they can be good starting points for modeling richer problems

By the end of the course, the students should be able to:

• understand how commercial software for solving optimization problems works
• understand how different ways to formulate optimization problems can affect the practical solvability of the problem
• assess when optimization models might be solved by exact methods and when heuristics are needed
• structure technical and economical planning problems so that they can be formulated as mathematical programs
• understand the pros and cons of different formulations and solution methods and the interaction between model and method
• implement and solve real technical and economical planning problems in commercial software and interpret the results

Besides this the course should give:

• advanced knowledge about how quantitative methods and models can provide decision support in technical and economical planning situations

Learning methods and activities

Lectures, seminars and exercises.

Compulsory assignments

• Exercises

Recommended previous knowledge

Master of Science in Industrial Economics and Technology Management with specialization in optimization, or similar.

Required previous knowledge

TIØ4130 Optimization Methods with Applications

Course materials

Syllabus literature will be given when the course starts.

Credit reductions