Metaheuristics are used to solve optimization problems quickly, but without mathematical guarantees. They have a wide application in industries such as logistics and production, as well as uses in NGOs and governments. Numerous heuristics and metaheuristics are presented and analyzed, such as Genetic Algorithms, Ant Colony Optimization, Simulated Annealing, Tabu Search, etc. Algorithm configuration and selection techniques are covered, as well as hybridized methods using these approaches within metaheuristics and mixed integer-linear solvers. When possible, guest lectures from industrial experts provide further insight into the application of course techniques in the real world.
Frequency | Weekday | Time | Format / Place | Period | |
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one-time | Di | 8-10 | W9-109 | 08.04.2025 | |
one-time | Di | 8-12 | W9-109 | 22.04.2025 | |
one-time | Di | 8-12 | W9-109 | 06.05.2025 | |
one-time | Di | 8-12 | W9-109 | 20.05.2025 | |
one-time | Di | 8-12 | W9-109 | 27.05.2025 | |
one-time | Di | 8-12 | W9-109 | 10.06.2025 | |
one-time | Di | 8-10 | W9-109 | 15.07.2025 |
Module | Course | Requirements | |
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31-M-ASM2 Advanced Statistical Methods II | Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (I.) | Graded examination
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Student information |
Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (II.) | Graded examination
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Student information | |
31-MM34 Data Science in Operations Research | Metaheuristics | Student information | |
31-MM34-WiMa Data Science in Operations Research | Metaheuristics | Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.