Transportation and Logistics Management

1 Syllabus

1.1 Practical Details

1.2 Teaching Team

1.3 Course Description

Logistics is the part of supply chain management (SCM) facing processes and decisions about the flow of raw materials, intermediate products, and finished goods (and their related information and services) from their respective origins to their destinations. One of its primary facets involves the design, scheduling, and routing of different modes of transportation (e.g., road, sea, etc.), thus well-defined and managed transportation networks aimed at delivering the right freights at the right time, quantity, and place.

Therefore, this course treats and studies relevant elements and components under the broad category of logistics and transportation by covering fundamental concepts, analytical approaches, and techniques for supporting the design and planning of logistics and transportation systems within supply chains. The course reviews different transportation means (i.e., road, rail, and sea), to provide the necessary knowledge and overall perspective permitting enhancement of the freight distribution of a given company or chain of them. The students attending this course will:

  • get a broad insight into transportation and distribution,
  • explore, develop, and use models for planning and scheduling transport and logistics operations, and
  • determine where and how specific principles and tools can be applied.

1.4 Learning Competencies

During this course, students will work on the following competencies and skills:

  • Planning and problem-solving
  • Team-working to solve logistics challenges
  • Discussing and presenting ideas and solutions
  • Being able to find information from different sources
  • Analyze and summarize qualitative and quantitative information

1.5 Learning Objectives

After successful completion of the course, the student is able to:

  • LO1: Explain logistics and transportation management strategies
  • LO2: Analyze and describe logistics systems
  • LO3: Differentiate and identify transportation problems within a given scenario
  • LO4: Examine and formulate logistical issues from case studies
  • LO5: Design, describe, and apply appropriate solution approaches for planning a given logistic system
  • LO6: Implement and assess methods for computer-aided planning (e.g., using a programming language)
  • LO7: Critically evaluate and describe a given solution approach
  • LO8: Write a well-structured report about a given logistic/transportation issue

1.6 Lectures: Flipped Classroom Format

The structure that we will follow for each lecture is as follows:

  • A video/audio lecture and the lecture handouts will be provided 2 or 3 days before the scheduled lecture.
  • You have to watch the prerecorded lecture before the lecture during your study time.
  • The 1st hour will be reserved to summarize and review the topics provided in the recorded lecture.
    • Questions about the material will be answered in this part.
    • This 1st hour does not necessarily use the whole hour; in such a case, the exercises of the 2nd-hour start earlier.
  • The 2nd hour of the scheduled lecture will be dedicated to discussing exercises.
    • To detect challenging concepts/exercises, you can email us before the lecture once you have watched the video.
    • The 2nd hour can be shorter, given that prerecorded material has to be watched in advance.

1.7 Tutorials and Practicals

The tutorial sessions are devoted to learning how to develop computational approaches in Python for solving logistics problems. You will learn how to:

  1. Break down a problem into smaller parts.
  2. Model the problem using exact and heuristic methods.
  3. Analyze the results.

The practical sessions are dedicated to supporting you in solving the assignments.

NoteTA-help.me

To ensure a sequential and smooth system for asking questions during the sessions, we will use TA-help.me, which is incorporated into Canvas (inside the left-side panel).

1.8 Canvas Discussions: Forum Guidelines

The Canvas Discussions forum aims to support your learning—ask questions, share insights, and help each other out! Engaging in discussions will deepen your understanding and make studying more effective. Feel free to start a new thread when needed, and jump in to answer if you know something that can help a fellow student. We’ll step in as needed to guide you.

To make the most of the forum:

  • Ask clear, specific questions, showing what you’ve tried.
    • Learning happens through problem-solving! If you’re asking about an exercise, share your approach so far. This helps others guide you effectively. Posts like “What’s the solution?” without any effort shown won’t get answers.
    • The same applies to tutorial and practical session questions—explain your reasoning so discussions stay meaningful. Direct requests for full solutions aren’t allowed since assignments are for practice, not just answers.
  • For assignment-related questions:
    • You can ask about interpretations or small coding issues, but avoid sharing full solutions or large code sections to prevent plagiarism. Keeping discussions conceptual helps everyone learn better.
  • Check before posting a new question.
    • If someone has already asked a similar question, continue the conversation in the same thread.
    • If not, go ahead and start a new one!
  • Prefer the forum over email for general questions.
    • This way, everyone benefits from the answers, and it keeps things organized.

1.9 Workload

In Table 1.1, you can find an overview of the estimated workload for this course.

Table 1.1: TLM is a 5 ECTS course, which corresponds to a workload of 140 hours. Please note that the workload is an estimate and may vary depending on the student’s background and skills.
Activity Number of Activities Estimated Time (h) Subtotal (h)
0 Lectures (incl. pre-lecture videos) 8 3 24
1 Guest Lecture 2 1 2
2 Python Tutorials 4 2 8
3 Practicals 8 2 16
4 Assignments 2 25 50
5 Test 1 3 3
6 Self-Study 8 4 32

1.10 Schedule

1.11 Content Overview

In the following, you can find the course’s schedule and the activities planned for each week. Be aware that the schedule is subject to change, please check TimeEdit and Canvas for the most recent updates.

The activities are divided into the following categories:

  • 📚 Lecture [x8]
    1. Introduction to Transportation & Logistics System Management
    2. Mathematical Modeling in Logistics: Vehicle Routing Problems
    3. Supplier Selection and Multi-objective Optimization in Logistics
    4. Planning and Decision-Support Approaches and Heuristics for Vehicle Routing
    5. Uncertainty and Disturbances in Logistics
    6. Transportation Network Design and Multimodal Transportation
    7. Collaborative Logistics: Game Theoretic Approaches
    8. Mock Exam
  • 🎤 Guest Lecture [x2]
    • Flying Fish (FF) - Rotterdam Water Taxi
    • Bolk Business Improvement (BBI)
  • 💻 Python Tutorial [x4]
    1. Introduction to Python, Version Control Systems (VCS), Agentic Programming
    2. Modeling a Transportation & Logistics Problem in Python (I): MILP, Gurobi
    3. Modeling a Transportation & Logistics Problem in Python (II): Multi-objective optimization
    4. Heuristics and Meta-Heuristics with Python for Static and Dynamic Problems
  • Assignment [x2] (AS1, AS2 + Repairs AS1-R, AS2-R)
    • 🟢 Release
    • 🟥 Deadline
    • 🙋 Practical (Q&A) [x8]
  • 📝 Exam
    • (O1) Opportunity 1
    • (O2) Opportunity 2
  • 📊 Grading Results

1.12 Schedule

See below the detailed schedule for the course.

Always check Canvas for times, rooms, and last-minute changes.
This section shows the sequence** and the key milestones (with the current planned dates).

Legend for the schedule’s symbols
Symbol Meaning
📚 Lecture
💻 Tutorial
🙋 Practical (Q&A / working session)
🎤 Guest lecture
📝 Exam
🟢 Release
🟥 Deadline

Milestones

Main course milestones (IMPORTANT: a grace period of 2 days is given for assignments’ submission)
Date Week Milestone Code
04/02/2026 w6 Assignment 1 released (in L1) 🟢 AS1
27/02/2026 w9 Assignment 1 deadline 🟥 AS1
04/03/2026 w10 Start Assignment 2 (kick-off practical) 🟢 AS2
05/04/2026 w14 Assignment 2 deadline 🟥 AS2
06/04/2026 w15 Study week (exam prep)
14/04/2026 w16 Written exam (Opportunity 1) 📝 O1

Phase 1: Assignment 1 (AS1) - Exact Optimization

Phase 1 schedule (Assignment 1 - Exact Optimization)
Week Date Session Type Topic / purpose
w6 04/02/2026 L1 📚 Syllabus, introduction
w6 04/02/2026 T1 💻 Intro to Python, VCSs, Agentic Programming
w7 10/02/2026 L2 📚 Mathematical modeling
w7 10/02/2026 T2 💻 Modeling in Python (I): MILP, Gurobi
w7 13/02/2026 GL1 🎤 Guest lecture (Flying Fish)
w8 17/02/2026 L3 📚 Supplier selection (multi-objective)
w8 18/02/2026 T3 💻 Modeling in Python (II): multi-objective optimization
w8 20/02/2026 P1 🙋 Practical (Q&A / working session)
w9 27/02/2026 D1 🟥 Deliver AS1

Phase 2: Assignment 2 (AS2) - Heuristics Optimization

Phase 2 schedule (Assignment 2 - Heuristics Optimization)
Week Date Session Type Topic / purpose
w10 02/03/2026 L4 📚 Planning & decision-support; heuristics for VRP
w10 04/03/2026 P2 🙋 Start AS2 (kick-off practical)
w11 10/03/2026 L5 📚 Uncertainty and disturbances in logistics
w11 12/03/2026 T4 💻 Heuristics & metaheuristics in Python for
w11 13/03/2026 P3 🙋 Practical (Q&A / working session)
w12 18/03/2026 GL2 🎤 Guest lecture (BBI)
w12 20/03/2026 P4 🙋 Practical (Q&A / working session)
w13 24/03/2026 L6 📚 Transportation network design & multimodal transportation
w13 27/03/2026 P5 🙋 Practical (Q&A / working session)
w14 31/03/2026 L7 📚 Collaborative logistics: Game-theoretic approaches
w14 01/04/2026 L8 📚 Practice exam
w14 01/04/2026 P6 🙋 Practical (Q&A / working session)
w14 02/04/2026 P7 🙋 Practical (Q&A / working session)
w14 27/04/2026 D2 🟥 Deliver AS2

Phase 3: Examination

Week Date Item Type Topic / purpose
w15 06/04/2026 Study week Exam preparation
w16 14/04/2026 Written exam 📝 Opportunity 1 (O1)
w19 08/05/2026 Repairs deadline 🟥 Deliver repairs for AS1 and AS2 (AS1-R, AS2-R)
w20 12/05/2026 Exam review 📝 Review session for O1
w27 02/07/2026 Written exam 📝 Opportunity 2 (O2)

1.13 Study Material

The primary study material used during the course is the following:

  • Lecture slides.
  • Journal papers and material are accessible from the UT intranet and indicated on Canvas or slides.
  • Exercise sheets.

The consultation book used for some parts of the course are:

  • Ghiani, G., Laporte, G., & Musmanno, R. (2013). Introduction to Logistics Systems Management (2nd ed., Wiley Series in Operations Research and Management Science). Wiley.
  • Gendreau, M., Potvin, J.-Y. (2019). Handbook of Metaheuristics (3rd ed.). Springer International Publishing.

1.14 Assessment

The course is assessed through a written exam and two practical assignments.

Exam

The exam covers all topics given during the course. Its duration is 3 hours (3h45, if extra time allowed).

Practical Assignments

Two practical assignments are proposed during the course. They have to be made in a team of 3 students. Students must enroll themselves in teams via Canvas -> People -> TLM Group to make teams. If you do not have a group, sign-in a group so other people looking for teammates can see you. The assignments require programming, analyzing, and reporting.

ImportantRepair Maximum Grade

If an assignment is not presented on the first try or fails (grade < 5.5), the maximum grade it can obtain in the second try (i.e., repair) is 5.5.

ImportantStrict Deadline

To ensure fairness, if an assignment is provided after its corresponding deadline, it will not be assessed.

Grading Validity

The validity of assignments’ grades can only be extended for one academic year. Namely, in case of not passing the exam, the marks for the assignments are still valid for the next academic course, given that all assignments have been finished successfully (grade equal to or larger than 5.5).

A student repeating the course may optionally decide if he/she wants to repeat or not all assignments. Notice that to save the assignments from one academic year to the next, all of them must have been completed and passed. Lastly, if you are repeating TLM and have the assignments already passed, communicate your case via email.

Composition of Grades

The final grade for this course is composed of:

  1. (55%) Assignments
    • (20%) Assignment 1.
    • (35%) Assignment 2.
  2. (45%) Written exam
    • The grade of the theory exam cannot be extended and has to be repeated in case of repeating the course.

To pass the course, you have to pass the exam (grade equal to or larger than 5.5) and the average of the exam and assignment grades have to be larger than 5.5.

  • If you passed the assignments but did not pass the exam, the exam’s grade will be the one reflected in Osiris, given that you will have to make a resit.
  • If you passed the exam and not the assignments, but the overall average is over 5.5, then you passed the course. However, if you don’t plan to repair the assignments because you passed the course, then the group has to let me know via email.
  • If you pass the exam but not the assignments and the overall average is lower than 5.5, then you can repair the assignments within their respective deadlines. However, if you don’t repair it, then in Osiris overall grade is reflected.

Second Chance and Assignments’ Repair Option

For the theory exam, a second chance is possible. In the case of assignments 1 and 2, repair options are possible:

  • The first chance for the exam is at the end of the quartile, and the second chance at the end of the 4th quartile. The highest grade is selected.
  • For each of the two assignments (i.e., assignments 1 and 2), a repair option is possible for those cases where the students fail to pass or submit the corresponding assignment.
  • The highest grade that can be obtained in the repair option is 5.5, and it is based on how well the students incorporated all the comments from their failed/no-submitted assignment.

1.15 Academic Integrity

Students at the University of Twente are responsible for knowing and being familiar with UT’s academic integrity policy. Such policy violations include cheating, plagiarism (wholly or partially), fabrication, lying, bribery, and threatening behavior. All incidents of academic misconduct are automatically reported to the UT Examination Board. Notice that specific software and careful checking are given to each assignment to detect fraud.

1.16 Policy on Generative AI

Following UT’s examination board, we encourage using ChatGPT and other Generative AI as the writing equivalent to a calculator to:

  • “get the ball rolling” on writing assignments;
  • get out of writer’s block;
  • write an outline;
  • improve the tone and language of an assignment.

Guidelines for Study Periods (tutorials, assignments)

  • Permitted Use: Students are allowed to use generative AI tools for learning, understanding concepts, and coding practice.
  • Penalty for Misuse: If AI is found to have been used extensively in an assignment, it will be considered academic misconduct and may result in penalties according to the institution’s rules.
ImportantUnderstanding Over Copying

Emphasis should be on understanding the AI-generated solutions, not just copying them. Students should be prepared to explain how and why a particular solution works (e.g., via an oral exam).

Guidelines for Assessment Periods

  • Prohibited Use: The use of generative AI tools is strictly prohibited during exams.
  • Penalty for Misuse: Any use of AI tools during assessments will be considered academic misconduct and subject to penalties as per the institution’s academic integrity policy.

1.17 Electronic Communication

  • Questions regarding organizational or personal matters? Please email the course coordinator ().
  • Questions regarding the course content, assignments, and tutorials? Please use the Canvas Discussions forum.

Students should expect a response to their communications within two business days.