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186.190 Optimization in Transport and Logistics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2018S, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Aim of course

Exact and heuristic optimization approaches for classical transportation problems.

Subject of course

Basics: modeling, algorithms, operations research, heuristic optimization. Advanced optimization techniques: branch-and-cut, branch-and-price, metaheuristics. Classical transportation problems: TSP, vehicle routing, shipment Didactics: - weekly lecture - exercise - oral exam

Additional information

Estimated Effort:

15h  Lecture
10h  Homework
15h  Programming Exercise I
15h  Programming Exercise II
20h  Exam Preparation and Exam
75h Total


  • Hiermann, Gerhard
  • Ritzinger, Ulrike Maria


Course dates

Thu10:00 - 12:0001.03.2018 - 28.06.2018Seminarraum 8 Lecture
Optimization in Transport and Logistics - Single appointments
Thu01.03.201810:00 - 12:00Seminarraum 8 Lecture
Thu08.03.201810:00 - 12:00Seminarraum 8 Lecture
Thu15.03.201810:00 - 12:00Seminarraum 8 Lecture
Thu22.03.201810:00 - 12:00Seminarraum 8 Lecture
Thu12.04.201810:00 - 12:00Seminarraum 8 Lecture
Thu19.04.201810:00 - 12:00Seminarraum 8 Lecture
Thu26.04.201810:00 - 12:00Seminarraum 8 Lecture
Thu03.05.201810:00 - 12:00Seminarraum 8 Lecture
Thu17.05.201810:00 - 12:00Seminarraum 8 Lecture
Thu24.05.201810:00 - 12:00Seminarraum 8 Lecture
Thu07.06.201810:00 - 12:00Seminarraum 8 Lecture
Thu14.06.201810:00 - 12:00Seminarraum 8 Lecture
Thu21.06.201810:00 - 12:00Seminarraum 8 Lecture
Thu28.06.201810:00 - 12:00Seminarraum 8 Lecture

Examination modalities

Attendance at practise sessions and oral exam at the end of the course

Course registration

Begin End Deregistration end
23.02.2018 00:00 01.04.2018 01:00 01.04.2018 01:00



  • Toth, Paolo, and Daniele Vigo, eds. Vehicle routing: problems, methods, and applications. Vol. 18. Siam, 2014.
  • Gendreau, M., & Potvin, J. Y. (2010). Handbook of metaheuristics (Vol. 2). New York: Springer.

Previous knowledge


  • knowledge of basic algorithms and data structures
  • knowledge of linear algebra and analysis, especially set theory, metrics, sequences and series