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SE Computational Logistics: Metaheuristik

Lecturer

 Dr. Fabien Tricoire, PhD (Johannes Kepler University, Linz)

Entry requirements

Knowledge required: ability to program

Course objectives

Students know the basic design concepts of heuristics and metaheuristics. They are able to design, implement and test simple heuristics and metaheuristics for planning problems arising in production, logistics and supply chain management. Furthermore, they know statistical methods for evaluating heuristic and metaheuristic results.

Course content

During this course, we will tackle the optimisation of difficult logistics problems such as routing optimisation, facility location or task scheduling. After emphasising the inherent difficulties in such problems, we will investigate various approximate optimisation algorithms, called metaheuristics, such as variable neighborhood search, adaptive large neighborhood search, tabu search, simulated annealing, genetic algorithms.

Assessment and Grading

Presentation of the project, exam

Required reading

Hoos/Stützle: Stochastic Local Search – Foundations and Applications, Elsevier 2005.

Gendreau/Potvin: Handbook of Metaheuristics, 2nd Edition Springer 2010.

Additional literature will be announced during the course.