[ 926LOMACLMS14 ] SE Computational Logistics: Metaheuristics

Workload Education level Study areas Responsible person Hours per week Coordinating university
6 ECTS M2 - Master's programme 2. year Business Administration Sophie Parragh 2 hpw Johannes Kepler University Linz
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)
On-site course
Maximum number of participants 25
Assignment procedure Assignment according to priority