This master’s specialisation focuses on those areas in the transport and logistics sector that require strong quantitative skills, which is typically where advanced decision support systems are in use. Examples are transport planning and scheduling in railways, shipping lines and airlines, revenue management, and supply chain design and inventory control. However, the quantitative skills can also be applied in other areas such as healthcare.
The programme consists of both methodological and applied quantitative courses. In the former you will learn advanced techniques in the field of Operations Research, while in the latter you will apply the techniques to challenging assignments and cases. With these skills you will be well-prepared to tackle real-life logistics business challenges.
The difference between this programme and standard logistics or supply chain management programmes is that you also learn the ins and outs of the methodology underlying advanced planning and decision support systems. This will provide you with a competitive edge, which is much appreciated by companies. The strong focus on methodology also prepares you well for a PhD trajectory.
Curriculum
The programme consists of five blocks each spanning eight weeks. The first three blocks are dedicated to courses and seminars, while the last two blocks will be mainly spent on writing a thesis. During the courses there will be assignments that you work on individually, while in the seminars you work in a team of students.
The curriculum consists of:
- 50 % Methodological courses
- 20 % Applied courses
- 30 % Seminars
In class
In the seminars you will work in a group of four students on a real-life case study where a logistic planning problem is being tackled. For instance, students work on rostering maintenance engineers at KLM. Such a roster contains duties with work, but also training and holidays. Rostering of the holiday season is the most challenging puzzle: on the one hand, there is a high demand for flights, while on the other hand, employees like to have their holidays in that period. Students should and were able to find a schedule, which balances these conflicting objectives, while satisfying all operational constraints.
Study schedule
The Take-Off is the introduction programme for all new students at Erasmus School of Economics. During the Take-Off you will meet your fellow students, get acquainted with our study associations and learn all the ins and outs of your new study programme, supporting information systems and life on campus and in the city.
Content:
- Renewal theory and regenerative processes
- Markov decision processes
- Q-learning
- Queueing networks
- Reinforcement Learning
(with applications to inventory and maintenance models)
Content:
- Advanced Polyhedral theory
- Column generation
- Dantzig-Wolfe decomposition
- Benders decomposition
- Branch-and-bound, Branch-and-price
- Valid inequalities, Branch-and-cut
Content:
- Introduction
- Regularization
- Trees, Forests and Ensemble Methods
- Support Vector Machines
- Clustering
- Neural Networks (Deep Learning)
- Reinforcement Learning
This content will be complemented with several assignments and readings.
The following inventory control models will be covered in the course:
- Single-location, single-item systems: standard (r,Q) and (s,S) policies with different demand distributions. Evaluation of service levels and costs. Optimization of policy parameters. Continuous and periodic review models. Deterministic and stochastic lead times
- Coordinated ordering: power-of-2 policies, joint replenishment model, production smoothing
- Multi-echelon systems: concepts and models, lot sizing, METRIC approach, guaranteed-service approach
In this course students should learn to design an algorithm in a structured way to find a solution to a mathematical programming problem with the focus on combinatorial optimization problems.
The topics covered in this course are:
- Complexity theory polynomial time algorithms
- Dynamic programming pseudo-polynomial time algorithms.
- Approximation algorithms
- Mathematical programming based heuristic (Matheuristic)
- Local search heuristics
- Evolutionary algorithms
This course deals with optimization problems where, in contrast to the deterministic models that the students encountered before, the problem data is uncertain. The techniques that the student will learn are related to the following list:
- Motivation and basic tools used: (i) second-order and semidefinite conic programming (ii) duality in conic/nonlinear programmes
- Robust optimization
- Stochastic optimization
- Chance-constrained optimisation (distributionally robust/stochastic)
Solving optimization problems, in the fields of transportation and scheduling, by
- adapting a given method to the optimization problem at hand,
- implementing the method, and
- analyzing the performance
The seminar covers the application of different Operations Research techniques to a real-world application presented by a company. The particular cases vary from year to year and include public transport planning, vehicle routing, inventory control, etc.
The seminar builds forth on the theory given in all other masters courses. Moreover, good programming skills are required.
Proposal for the Master thesis Econometrics and Management Science. This proposal can be used as a part of the Master thesis. There is no grade for this proposal.
The thesis is an individual assignment about a subject from your Master's specialisation. More information about thesis subjects, thesis supervisors and the writing process can be found on the Master thesis website.
Disclaimer
The overview above provides an impression of the curriculum for this programme for the academic year 2025-2026. It is not an up-to-date study schedule for current students. They can find their full study schedules on MyEUR. Please note that minor changes to this schedule are possible in future academic years.