ORL-33306 Decision Science for Technology


Credits 6.00

Teaching methodContact hours
Independent study0
Course coordinator(s)dr. ir. GDH Claassen
Lecturer(s)dr. ir. GDH Claassen
dr. D Krushynskyi
Examiner(s)dr. ir. GDH Claassen
dr. D Krushynskyi

Language of instruction:


Assumed knowledge on:

ORL-20306 Decision Science 1; fundamentals of programming (e.g. INF-22306 Programming in Python)

Continuation courses:

ORL-30306 Decision Science 2
ORL-30806 Operations Research and Logistics 


Decision Science for Technology (DST) broadens and builds on the fundamentals of Decision Science 1 with a strong focus on design oriented, quantitative decision support for its application in technological domains. The global aim of the course is three-sided:
- t
o broaden and deepen the acquired modelling skills in order to obtain and maintain effective proficiency. Formulating effective mathematical programming models needs a certain degree of ingenuity and can be mined by delivering structured theory illustrated by examples, offering small scale exercises, and finally apply the acquired knowledge on real-life cases. DST focuses specifically on modelling techniques for non-linear programming, integer programming, and multi-criteria decision making, foremost applied in engineering and technological domains;
- solving optimization problems is another aspect that needs attention. Here we cannot simply rely on well-programmed and advanced computer software. Elementary knowledge and understanding of the underlying solution techniques is needed to apply and interpret the outcome successfully in practice. Moreover, problems in practice are often too large and/or difficult to find optimal solutions. DST pays attention to both common sense- and mathematical programming based approximation methods (e.g. by applying aggregation-, decomposition-, or reformulation principles, and/or heuristics) to find effective solutions within a reasonable amount of time. Basic understanding of the logic behind various concepts provides an indispensable foundation for a successful application and correct interpretation of the generated solutions;
- the application and validation of OR-based models and solution techniques for concrete case-studies in engineering and technological domains. This includes an (indispensable) technical and efficient connection to large data sets and tailored reporting modules.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- transform a given problem description into a mathematical programming model;
- select appropriate solution techniques to solve the formulated problem(s);
- a
pply approximation methods for solving large-scale problems;
- deduce a coherent modelling approach for decision problems in practice;
- analyse the outcome of developed models for a business case from practice;
- evaluate the efficiency and the effectiveness of a modelling and solution approach.


- following lectures and study the written material;
- making exercises and receive feedback during tutorials;
- acquiring skills, understanding and insights during computer labs;
- analyzing and diagnosing a business case.


- closed book, written exam (70%);
- computer practicals (30%) ; to be completed with at least a 5.5.


Decision science; Theory and applications, Chapter 9: Modelling techniques for (non)linear and (mixed) integer programming. G.D.H. Claassen et al., 2007. ISBN: 978-90-8686-001-02.

Additional learning materials, e.g. articles and handouts will be provided via Brightspace.

Restricted Optional for: BATBiosystems EngineeringBSc4WD