Entry requirements

120 credits including 90 credits in mechanical engineering, industrial engineering, production engineering, automation engineering and/or computer science/information technology; 5 credits in computer programming; 20 credits in mathematics; 5 credits in statistics and probability theory. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course the student shall be able to

• formulate industrial optimisation problems as effective mathematical models,
• describe and explain the main concepts of the optimisation algorithms in the course,
• identify and analyse optimization problem properties such as soft and rigid constraints, objectives, and valid assumptions,
• use available software for solving optimisation problems in the industrial applications area,
• develop and apply (meta-) heuristics for certain optimization problems,
• compare advantages and disadvantages with exact solutions versus approximate solutions for different optimization problems.

Content

Optimisation methods and their application, with focus on (non-) linear mathematical models, including formulating, solving and analysing of such models. Exact and (meta-) heuristic algorithms. Implementation and test of optimisation methods with the use of software for solving different types of industrial problems.

Instruction

Lectures, computerized laboratory work and supervision of project work.

Assessment

Written examination and oral and written presentation of project work.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.