TAOP63 · 6 credits · Advanced level (A1N) · Matematik, Tillämpad matematik
Optimization of Complex Systems
Active · next start HT 2026
100%
passed the latest regular sitting
PRA1 · 26 Jan 2026 · 13 students
100%
passed the regular sittings, all sittings over the last 5 years
PRA1 · 13 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Hand-in or lab
PRA1 Project work
- G
Main rounds
100% passed · 13 students
- 100%13
Later rounds
No sittings with at least 5 students.
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Other module
MUN1 Oral exam
- 5
- 3
Main rounds
100% passed · 11 students
- 100%11
Later rounds
No sittings with at least 5 students.
- Get 5: 18% (11 students)
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%1
Who takes the course?
- Master of Science in Computer Science and Engineering
- Master of Science in Energy - Environment - Management
- Master of Science in Energy - Environment - Management
- Master of Science in Energy - Environment - Management
- Master of Science in Energy - Environment - Management
- Master of Science in Information Technology
- Master of Science in Chemical Biology
- Master of Science in Chemical Biology
- Master of Science in Engineering Biology
- Master of Science in Engineering Biology
About the course
Aim
The course treats mathematical tools for formulation and solving of optimization problems for realistic complex systems, including environment and energy aspects. Both advanced model formulation and choice of solution method based on the problem structure are included. Methods used are heuristics and decomposition methods.After finished course, the student shall be able to:Formulate complicated optimization problems as mathematical models.Identify structures in complex model formulations.Choose suitable solution method based on the problem structure, and motivate the choice.Explain the principles behind certain heuristic solution methods and decomposition methods, and use them to solve problems.Plan, develope and realize certain advanced solution techniques for complex optimization problems.Use general and specialized software for optimization.Present results orally and in writing.
Content
Advanced model formulation, metaheuristics, heuristics for combinatorical problems, methods for expensive objective functions, decomposition methods based on Lagrange relaxation. Examples of formulations containing environment and energy aspects.
- Examiner
- Nils-Hassan Quttineh
- Scheduled hours
- 50 h
- Main field of study
- Matematik, Tillämpad matematik