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| Detailed information |
| Original study plan |
Master's programme Business Informatics 2025W |
| Learning Outcomes |
Competences |
| Students know the existing heuristic and metaheuristic solution concepts that are used in logistics applications and can adapt them to a selected problem, implement and apply them to data sets, evaluate them statistically and derive appropriate recommendations for action.
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| Skills |
Knowledge |
- Learning Outcome 2 (LO2): explain the key ideas of metaheuristic solution methods.
- Learning Outcome 3 (LO3): develop a metaheuristic approach for a chosen problem on their own.
- Learning Outcome 4 (LO4): implement a developed metaheuristic approach in an appropriate programming language.
- Learning Outcome 5 (LO5): apply, statistically evaluate and validate their approach on data sets.
- Learning Outcome 6 (LO6): describe the developed method in written as well as oral form, according to scientific criteria, in a structured and easily accessible way and to prepare the obtained results in order to derive recommendations for action.
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- Learning Outcome 1 (LO1): Recall the most important metaheuristic concepts: Variable Neighborhood Search, Adaptive Large Neighborhood Search, Tabu Search, Simulated Annealing, Genetic Algorithms
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| Criteria for evaluation |
In total, students have the possibility to reach 100 points (project work + presentation).
1. Exam: After the first content part, there is a written exam on the covered content.
2. Project work: a topic is developed in small groups, a solution procedure is implemented and results are generated and summarized in a seminar paper.
3. Presentation: the problem, solution method and results are presented.
Synchronization of learning outcomes and assessment:
- LO1: Exam
- LO2: Project work+presentation
- LO3: Project work+presentation
- LO4: Project work+presentation
- LO5: Project work+presentation
- LO6: Project work+presentation
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| Methods |
The course uses a combination of different teaching methods in order to
1. maximize the motivation and attention of the students.
2. address the learning objectives in the didactically best way.
This includes the following
- Teacher-centred information inputs, supported by slides and literature
- Development of content in collaboration with the students.
- Development of a topic as part of project work in small groups
- Group discussion.
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| Language |
English |
| Study material |
Burke, Kendall: Search Methodologies, Introductory Tutorials in Optimization and Decision Support Techniques. Springer. 2005
Gendreau, Potvin: Handbood of Metaheuristics, 2nd Edition. Springer. 2010.
Hoos, Stützle: Stochastic Local Search - Foundations and Applications. Elsevier. 2005.
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| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WCLOME: SE Computational Logistics: Metaheuristics (2013W-2014S)
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