Supply Chain Lab


Course Objective

The course has the following five main learning objectives:
- Apply and test network optimization and integer programming techniques
- Formulate optimization models using algebraic modelling language
- Solve integer and network optimization problems using dedicated
- Analyze real-life industry problems, formulate and defend managerial
- Assess the capabilities and limitations of optimization models to
solve real-life decision making problems arising in distribution

Course Content

Transport and distribution management often consists of interrelated
decisions involving network design, production planning and scheduling,
product storage and warehousing, transport and distribution. This course
introduces students to these specific decision problems, and also equips
them with tools and methods to solve these problems.

Mixed Integer Linear Programming (MILP) is a powerful tool for improving
transport and distribution decision processes. As MILP is being used
more and more often in the industry, the course wants to offer students
key insights and sufficient training to be able to contribute in
development and implementation projects in their professional career.
To this end, dedicated software packages are introduced for modeling and
solving distribution problems via MILP.

This course extends the scope of the Decision Making in Supply Chains
course to more complex optimization problems arising in real-life
transport and distribution planning. Solution approaches are illustrated
by means of a selection of topics, e.g. transportation planning,
facility location, network design, vehicle routing and scheduling,
manpower planning, and rostering.

The teaching and learning are largely based on "learning by doing" with
a number of cases in different industrial applications. A mixture of
hearing lectures, tutorials, assignments and case studies offers
students the best possible support to master sufficient skills to tackle
real-life cases in distribution logistics.

Teaching Methods

Practical tutorials

Method of Assessment

Based on an individual computer-based exam and a course group project.


Anderson, D.R., et al. (2016). An Introduction to Management Science:
Quantitative Approaches to Decision Making

I W.L. Winston, S.C. Albright (2018). Practical Management Science

Target Audience

The course is designed for master students in Transport and Supply Chain

Recommended background knowledge

Decision Making in Supply Chain
... or some lectures about basics of (Integer) Linear Programming and
Mathematical Modeling

General Information

Course Code E_BA_SCL
Credits 6 EC
Period P4
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. R. Roberti
Examiner dr. R. Roberti
Teaching Staff dr. R. Roberti

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: