| Detailed information |
| Original study plan |
Master's programme Business Informatics 2019W |
| Objectives |
Students have a competent knowledge of existing heuristic and metaheuristic methods used in logistics applications. They know basic design concepts of heuristics and metaheuristics. They are able to design, implement and test simple heuristics and metaheuristics for planning problems on their own. Furthermore students know statistical methods for evaluation of heuristic and metaheuristic results.
|
| Subject |
metaheuristic concepts: Variable Neighborhood Search, Adaptive Large Neighborhood Search, Tabu Search, Simulated Annealing, Genetic Algorithms, Ant Colony Optimization.
|
| Criteria for evaluation |
Talk on the project thesis, examination
|
| Methods |
home exercises, project thesis
|
| 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.
|
| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WCLOME: SE Computational Logistics: Metaheuristics (2013W-2014S)
|
