| Day | Date | Time | Room |
|---|---|---|---|
| Thursday | 11/12/26 | 09:00 AM - 12:30 PM | D2.0.030 |
| Thursday | 11/19/26 | 09:00 AM - 12:30 PM | D2.0.030 |
| Tuesday | 12/01/26 | 09:00 AM - 12:30 PM | D2.0.030 |
| Thursday | 12/03/26 | 09:00 AM - 12:30 PM | D2.0.030 |
| Thursday | 12/10/26 | 09:00 AM - 12:30 PM | D2.0.030 |
| Thursday | 12/17/26 | 09:00 AM - 12:30 PM | D2.0.030 |
| Wednesday | 01/13/27 | 10:30 AM - 12:30 PM | TC.3.21 |
This course addresses optimization topics in transport and logistics, providing a comprehensive overview of problems and their practical applications. We begin by studying well-known basic problems such as the Travelling Salesman Problem and the Vehicle Routing Problem. Throughout the course, students will learn a variety of solution methods, including both exact algorithms and heuristic approaches, to effectively tackle these complex challenges.
Beyond these classical problems, the course explores important extensions and variants that arise in real-world scenarios. Additionally, we will examine optimization techniques applied to warehousing, railway systems, and public transport, highlighting how these methods improve operational efficiency and service quality in diverse transportation contexts.
By integrating theoretical foundations with practical applications, this course equips students with the tools to analyze and solve a wide range of optimization problems in transport and logistics, preparing them for challenges in both academic research and industry practice.
After successful completion of the module, students should know how they can apply quantitative tools to improve decision-making in the context of transport and logistics. Practical knowledge concerning the usage of optimization and simulation tools will be acquired.
Attendance in the first lecture is mandatory for the participation in the course. If an absence cannot be avoided inform the lecturer before class and provide some form of proof for the absence (e.g. medical confirmation) in the next session. In total, a minimum requirement for attendance of 80% is required to pass the course.
If the attendance falls below 80% for students receiving partial credit, students are graded with 5 (Nicht genügend).
The course is taught using a combination of lectures, class discussions, and homework assignments.
Your performance will be evaluated on the basis of one exam, one case study and homework assignments. The grading of the course will be as follows:
Grading scale:
Prerequisite for passing the course: minimum performance of 40% in the final examination.
The use of AI tools of any kind for course work assignments must be documented and declared in a statement in the introduction to documents submitted for assessment (e.g., texts, presentation slides, video/audio materials).
You will work in teams of X students on one case study. Every member of your team will receive the same score, subject to controls for "free riding".
Incoming students (exchange programs): min. 5 ECTS credits in logistics management.
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COURSE CONDUCT AND EXPECTATIONS:
The course is based on lectures, cases, and application exercises. You are expected to have completed assignments, read the assigned material, and rework class exercises and demonstrations after each class session. If your expectations for the course are not being met or if you are concerned about your grade or other course related matters, please talk to your instructor as soon as possible during the semester.
COMMUNICATION CHANNELS:
Lecture notes, grades, and other informat ion related to the class will be posted throughout the semester in Canvas. Emails will be sent to students’ university email accounts.