The learning outcomes of the course on Transport and Distribution
Planning are the following:
In this course, the students learn the challenges faced when optimizing
Transport and Distribution plans. Such plans often require the use of
heuristics to be efficiently established. The course addresses the most
important and powerful optimization techniques known, with emphasis on
those techniques that work well for real-life planning.
The students learn how to distinguish heuristics in terms of efficiency,
solution quality and other quantitative aspects.
The students will become able to solve challenging practical problems,
which are within the realm of professionals only.
In this course, the students work in teams. They learn how to combine
different skills, backgrounds and interests to solve challenging and
Link to practice
The lecturers have a sound practical record, complementing their
extensive academic achievements. A representative choice of illustrative
problem domains from practice is therefore guaranteed, as well as a good
understanding on how to bridge the gap between theory and practice.
Furthermore, the optimization techniques taught are among the best used
• Heuristics form an indispensable tool for everyone working in
operations management, and in the planning of Transport and Distribution
• Problems arising from practice are often too hard to solve exactly and
heuristics are relatively simple methods that may provide feasible
solutions of good quality.
• The course covers two areas: the first is about heuristic ideas
applicable to general problems and the second is focused on the
application of heuristics to Transport and Distribution problems. This
field, of so-called routing problems, is so rich that virtually all
published heuristic ideas have been applied to it.
• The course is further divided into three parts, each of which first
covers general problems and then focuses on routing.
• These three parts are:
• Classical heuristics to construct a feasible solution
• Improvement heuristics based on structured local search
• Heuristics aiming at escaping local optima
Regardless of the part being addressed attention is paid to:
• Meta-heuristics, i.e., general ideas applicable to a large variety of
• Complexity analysis
• Whether a performance guarantee can be given and how to prove it
• Ways to benchmark and empirically assess quality
Form of tuition
Type of assessment
Written exam – Individual assessment
(Interim) Assignment(s) – Group assessment
• Talbi, El-Ghazali (2009). Metaheuristics: From Design to
• Toth, P. and Vigo, D. (2002). The Vehicle Routing Problem, 1st
• Toth, P. and Vigo, D. (2014). Vehicle Routing: Problems, Methods and
Applications, 2nd edition. SIAM.
complemented with slides and additional notes to be provided
A quantitative background with some affinity with computer programming
Recommended background knowledge
Applied mathematics, econometrics, engineering, business administration,
computer or data science, management sciences, or any quantitative study
Both BA/IE students with an interest in optimization and OR/AM students
with an interest in computer implementations can participate, since they
will work together in teams combining knowledge and skills.
The students will experience optimization techniques. We will make as
much use of the language R as possible to express the optimization
algorithms addressed in the course. R is used through the minor,
therefore learning it will be useful for several courses, and you may
find it useful in your careers as well.