TU Delft
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2017/2018 Technology, Policy and Management Master Engineering and Policy Analysis
Advanced Discrete Simulation
Module Manager
Name E-mail
Prof.dr.ir. A. Verbraeck    A.Verbraeck@tudelft.nl
Name E-mail
Dr. Y. Huang    Y.Huang@tudelft.nl
Prof.dr.ir. A. Verbraeck    A.Verbraeck@tudelft.nl
Responsible for assignments
Name E-mail
Prof.dr.ir. A. Verbraeck    A.Verbraeck@tudelft.nl
Co-responsible for assignments
Name E-mail
Dr. Y. Huang    Y.Huang@tudelft.nl
Contact Hours / Week x/x/x/x
Education Period
Start Education
Exam Period
Course Language
Required for
SEN9110 - Simulation Masterclass (elective) can be followed after this course.
Expected prior knowledge
Basic knowledge about discrete modeling and simulation, data analysis, probability and statistics. Basic programming skills in Python or Java.
Course Contents
The objective of this course is to provide its participants with a selection of topics that are state-of-the-art in discrete simulation. The course will focus on the theoretical foundation of (potentially large-scale) distributed data-driven simulation, as well as the corresponding methods and techniques. The course provides a set of lectures, in-class discussions in combination with lab exercises and group assignments. The participants are required to individually prepare the classes with reading assignments according to the announcements on Brightspace. In parallel, groups of students carry out lab exercises for the topics that have been studied. The lab exercises will be discussed with the teachers and marked.

The following topics will be covered in the course:
1. Introduction to large-scale, distributed discrete-event simulation;
2. Data-driven simulation and data quality; providing input and structure for large-scale simulations;
3. Component-based simulation; providing the building blocks for large-scale simulations;
4. Metamodeling; making sure all components are based on the same definitions;
5. Advanced visualization methods for large-scale simulations; providing the output;
6. Experimentation and experimental design; parallel execution of simulation models; cloud simulation;
7. Distributed simulation; making multiple components work together;
8. Examples of large-scale, distributed simulations; the practical relevance;
9. Presentations and feedback on the lab exercises.
Study Goals
After successfully completing the course, the participants will be able to:
- Describe important topics in advanced discrete simulation and explain their implications in practice;
- Explain the theoretical foundation of advanced discrete simulation methods, their abilities and differences;
- Apply a set of advanced discrete simulation methods to case assignments for experimentation and analysis;
- Reflect on the strengths and limitations of the methods;
- Hypothesize on possible future developments in the discrete simulation discipline.
Education Method
A mix of lectures, in-class discussions, pre-class reading assignments, supervised lab exercise sessions, and hand-in group assignments. Groups are not to exceed four members.
Computer Use
Supervised lab exercise sessions where students will work with large-scale data-driven simulation on their own computers. Several tools that are needed for the course (e.g., Simio, Python, MySQL) will be made available.
Course Relations
This course is an excellent preparation for SEN9110 - Simulation Masterclass.
Graduation project using large-scale (discrete-event) simulation can benefit from this course.
Literature and Study Materials
No textbook is required. A set of scientific papers and book chapters will be made available through Brightspace.
The grading for this course will be based on the result of group assignments (40%) and on a final written exam (60%). Both parts must be completed with a passing grade (at least 5.75) in order to pass the course.
Permitted Materials during Tests
All written materials (papers, slides, notes, books) will be permitted during the written exam.