Curriculum that combines quantitative expertise with Real-World logistics challenges
The Analytics and Operations Research in Logistics specialisation focuses on areas within transport and logistics that rely heavily on advanced decision support systems. You will learn how to apply and develop quantitative methods to optimise planning, scheduling, and resource allocation in sectors such as railways, shipping, aviation, and supply chain management. These skills are also transferable to other domains, including healthcare and energy.
Programme structure
The programme is structured across five blocks of eight weeks.
- Methodological courses provide a deep understanding of operations research techniques, machine learning and mathematical modelling.
- Applied courses challenge you to use these techniques in realistic business scenarios.
- Seminars offer team-based learning through case studies that simulate complex logistics problems.
- Master thesis is written individually in the final blocks, based on your own research and under close supervision.
Curriculum overview
- 50% Methodological Courses
- 20% Applied Courses
- 30% Seminars
The exact composition depends on your course selection.
In class
You will work on real-life cases that require both analytical precision and strategic thinking.
For example:
How can an KLM schedule its maintenance engineers during peak travel seasons?
In a seminar project, you will develop a roster that balances operational needs with employee preferences, using optimisation techniques to satisfy constraints while improving efficiency. You will work in teams to analyse data, model the problem, and present a solution that could be implemented in practice.
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
This overview provides a general impression of the 2026-2027 curriculum. It is not the current study schedule. Enrolled students can find the most up-to-date version on MyEUR. Please note that minor changes may occur in future academic years.