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Technische Universität München

Transportation Analytics (Master Program)

(Prof. Dr. Stefan Minner, Nicolas Kuttruff, Till Krieger)
Mon 15:00-18:00 in 0514

Announcements

The theoretical background corresponds to material from the course Transportation Logistics. Recorded lectures are provided via Moodle for students who need to acquire or refresh this knowledge. The course includes an intermediate presentation and a final group project presentation. 

Course Description

  • The course focuses on modelling, solving, and implementing optimization and analytics problems in transportation.
  • The main transportation fields are:
    • Air Transport
    • Railway
    • Maritime
    • Vehicle Routing
  • Application sessions additionally cover:
    • Python and Gurobi
    • Network Design
    • Machine Learning in Transportation
    • Algorithm Design with LLMs
  • Course material and recorded lectures are provided via Moodle. 

Course Structure

  • Application sessions on Mondays
  • Transportation Logistics (Bachelor)
    • Theoretical knowledge from this course is required
    • Missing knowledge can be acquired through the provided recorded lectures
  • Students work in groups of 2-3 in one selected transportation field
  • Intermediate presentation
  • Final project presentation 

Learning Objectives

Students gain deeper insights into optimization and analytics problems in transportation. They learn how to formulate mathematical models, implement and solve them using Python and Gurobi, and analyze the resulting solutions. They also learn to apply these methods to realistic transportation problems using real-world data and gain experience with machine learning and algorithm design.

Methods

The course consists of practical application sessions focusing on mathematical modelling, implementation, and data-driven methods. Theoretical background is provided through recorded lectures from Transportation Logistics. Students apply the methods first to synthetic instances and subsequently develop a realistic case study using real-world data. 

Grading

The grading is based on two group presentations:

  • Intermediate presentation: 25%
  • Final project presentation: 75% 

For the final project, students develop a realistic case study, use real-world data, and submit their code together with a marimo notebook. 

Literature

  • Toth, P., & Vigo, D. (Eds.). (2014). Vehicle Routing: Problems, Methods, and Applications. 2nd ed., SIAM.
  • Williams, H. P. (2013). Model Building in Mathematical Programming. 5th ed.
  • Hillier, F. S., & Lieberman, G. J. (2015). Introduction to Operations Research. 10th ed., McGraw-Hill.  
  • Barnhart, C., & Laporte, G. (Eds.). (2007). Transportation. Handbooks in Operations Research and Management Science, Vol. 14. North-Holland.
  • Liu, F., Yao, Y., Guo, P., Yang, Z., Lin, X., Zhao, Z., ... & Zhang, Q. (2026). A systematic survey on large language models for algorithm design. ACM Computing Surveys, 58(8), 1-32.
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