Freight Transport Management spring 2024


26 February, 11 March 2024


10.00 -16.00 h.




Prof. Kees Jan Roodbergen and Dr. Ilke Bakir




4 (attendance + passing assignment) (2 course days + individual coaching)

Course fee:

Free for TRAIL/Beta/ERIM/OML members, others please contact the TRAIL office


see below


In this course you will learn to:

  • describe transportation networks, logistics operations and distinguish between related synchronization issues in the network;
  • design and apply models and solution approaches for port logistics and transportation;
  • design and apply mathematical models and solution approaches to solve specific decision problems such as vehicle routing.

Course description:

The aim of this course is to learn how to plan and control transport operations in supply chain networks through algorithms. We study how to design and apply solution approaches to deal with typical decision problems that arise in transportation networks to make sure that the presented objectives will be met. In this course, we show both qualitative and quantitative approaches to reach this goal for freight transportation. We study several types of facilities in more detail such as ports and cross-docking facilities. We treat various decision problems at the tactical and operational levels and examine supply chain synchronization issues in more detail. Examples include port logistics, vehicle routing in hinterland transportation networks and vehicle routing. Several modelling techniques are addressed to show how to tackle these decision problems and how to deal with uncertainty in the network. We discuss several important trends such as synchromodal transportation networks and new mathematical formulations. An important part of the course consists of hands-on learning by designing and software coding of an algorithm for a freight transport problem.


Each student will develop a research project throughout the course. Through this assignment, students will learn to design solution methods for very large optimization problems in Operations Research (such as Adaptive Large Neighborhood Search). The focus will be on deterministic, NP-hard problems. Students will learn to create a mathematical representation of such problems, to identify promising approaches from academic literature, to design solution methods, to implement solution methods in software, and to report on the design and results. Students will learn the skills to identify promising avenues and instrumental usage of available scientific knowledge for solving problems at hand. Individual coaching will be provided to the students by the teachers to help guide the algorithmic development.




The course provides an overview of qualitative and quantitative methods for addressing contemporary transportation problems. The assignment focusses on developing and implementing metaheuristics for solving combinatorial optimization problems.

Course material:

Lecture 1:

  • Carlo, H.J., Vis, I.F.A., Roodbergen, K.J. Transport Operations in Container Terminals: Literature Overview, Trends, and Research Directions, European Journal of Operational Research 236, 1-13.
  • Rose, W.J., Bell, J.E., Griffis, S.E. (2023). Inductive research in last-mile delivery routing: Introducing the re-gifting heuristic. Journal of Business Logistics 44(1), 109-140.
  • Laporte, S. Ropke, T. Vidal (2014). Heuristics for the vehicle routing problem. In Vehicle routing: problems, methods, and applications. Toth, P. and Vigo, D. (editors). Society for Industrial and Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, pages 87-116.

Lecture 2:

  • Semet, P. Toth, D. Vigo (2014). Classical exact algorithms for the capacitated vehicle routing problem. In Vehicle routing: problems, methods, and applications. Toth, P. and Vigo, D. (editors). Society for Industrial and Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, pages 37-57.
  • Sacramento, D., Pisinger, D., Ropke, S. (2019). An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones. Transportation Research Part C: Emerging Technologies 102, 289-315.


Master courses on Operations Research and Logistics. Students should have MSc level knowledge of modeling, mathematical programming, heuristics and computer implementation. Note that the assignment requires a significant effort in software coding of an optimization method for a distribution problem. Each team may select its own preferred programming language (C++, Python, R, Java, …) based on their past experience.

Course Registration form

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