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[ 926LOMACLOS14 ] SE Computational Logistics: Optimization
Versionsauswahl
Es ist eine neuere Version 2020W dieser LV im Curriculum Master's programme Business Informatics 2023W vorhanden.
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 competent knowledge of existing exact solution concepts used in logistics applications. They know concepts for modelling mixed integer programs. They are able to design mixed integer programs for logistics applications on their own. Furthermore they are able to design, implement and test simple opimization techniques for planning problems. They also know how to evaluate results.
Subject Modelling of mixed integer programs, realization with a commercial solver, branch and bound, branch and cut, column generation, branch and price.
Criteria for evaluation Talk on the project thesis, examination
Methods home exercises, project thesis
Language English
Study material Suhl, Mellouli: Optimierungssysteme, Springer, 2006.

Grünert, Irnich: Optimierung im Transport, Band I, Grundlagen, Shaker Verlag, 2005.

Korte, Vygen: Combinatorial Optimization - Theory and Algorithms, 5th Edition. Springer, 2010.

Desaulniers, Desrosiers, Solomon: Column Generation, Springer, 2005.

Additional material will be announced during class.

Changing subject? No
Earlier variants They also cover the requirements of the curriculum (from - to)
2WCLOOP: SE Computational Logistics: Optimization (2013S-2014S)
On-site course
Maximum number of participants 25
Assignment procedure Assignment according to priority