040247 KU Transportation Analytics and Optimization Tools (MA) (2023S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 13.02.2023 09:00 to We 22.02.2023 12:00
- Registration is open from Mo 27.02.2023 09:00 to Tu 28.02.2023 12:00
- Deregistration possible until Fr 17.03.2023 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 02.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 09.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 16.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 20.03. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 23.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 27.03. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 30.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 17.04. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 20.04. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 24.04. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 27.04. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 04.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 11.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Friday 12.05. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 22.05. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 25.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 01.06. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 05.06. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 15.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Monday 19.06. 13:15 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 22.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Thursday 29.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Friday 30.06. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
Information
Aims, contents and method of the course
Assessment and permitted materials
[20%] Midterm written exam
[20%] Final written exam
[30%] Group project with presentation in class. Students can choose between two types of project tasks:
a) Implementation: Solve a vehicle routing problem with a method learned in the lecture (via Excel Solver, modelling language and MIP-solver, or coding) and conduct a computational study.
b) Literature Work: Present a new variant of the vehicle routing problem, discuss applications and shortcomings, and explain how solution methods covered in the lecture must be adapted to deal with the problem.
[30%] Homework assignments
[20%] Final written exam
[30%] Group project with presentation in class. Students can choose between two types of project tasks:
a) Implementation: Solve a vehicle routing problem with a method learned in the lecture (via Excel Solver, modelling language and MIP-solver, or coding) and conduct a computational study.
b) Literature Work: Present a new variant of the vehicle routing problem, discuss applications and shortcomings, and explain how solution methods covered in the lecture must be adapted to deal with the problem.
[30%] Homework assignments
Minimum requirements and assessment criteria
At least 50% of the overall total achievable score must be obtained for a positive grade.
Examination topics
Lecture and exercise notes, literature excerpts, usage of optimization tools
Reading list
Toth, P. and Vigo, D.: Vehicle Routing: Problems, Methods, and Applications. Philadelphia: SIAM, 2014. –ISBN 978-1-611973-58-7
Association in the course directory
Last modified: Tu 14.03.2023 11:28
The course focus on state-of-the-art components of exact as well as heuristic solution methods. Regarding exact methods, we learn about the components of branch-price-and-cut algorithms. On the heuristic side, local search methods, metaheuristics, and large neighborhood search are covered. We will learn that for both, heuristic and exact methods, the solution of variants of the shortest path problem with resource constraints via dynamic program is a key component. In the exercise part of the course, we will learn how solution methods can be implemented by using different optimization tools. These tools will be applied in several homework assignments.
A basic OR knowledge is strongly recommended. Experience in using modelling languages or coding is beneficial. According to the flipped classroom principle, students will prepare a presentation in groups and present in class.