Bachelor
Informatica
Informatiekunde
Kunstmatige intelligentie
Master
Computing Science
Game&Media Technology
Artifical Intelligence
Business Informatics

Website: | website containing additional information | ||||||||||||||||||||||||||||||||

Course code: | INFOMADS | ||||||||||||||||||||||||||||||||

Credits: | 7.5 ECTS | ||||||||||||||||||||||||||||||||

Period: | period 1 (week 36 through 45, i.e., 5-9-2016 through 11-11-2016; retake week 1)
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Timeslot: | C | ||||||||||||||||||||||||||||||||

Participants: | up till now 45 subscriptions | ||||||||||||||||||||||||||||||||

Schedule: | Official schedule representation can be found in Osiris | ||||||||||||||||||||||||||||||||

Teachers: | Dit is een oud rooster!
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Exam: |
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Contents: |
In many real-life decision problems in e.g. (public) transportation, logistics, energy networks, healthcare, computer networks and education we want to select a very good solution from a large set of possible solutions. In the course you learn how to model such problems and how to solve them by well-known (simulation) algorithms. We focus on discrete models. You learn about the theoretical complexity and about the possibilities for exact optimization algorithms, heuristics and what-if analysis.
For stochastic problems, we study The learning outcomes of the course are - Knowledge of discrete-event simulation models and combinatorial optimization models
- Knowledge of methods for experimental research with discrete-event simulation including statistical methods
- Insight in the complexity of combinatorial optimization problems
- Knowledge of well-known types of combinatorial optimization algorithms
- Ability to model problems from applications as a discrete-event simulation problem and as a combinatorial optimization problem
- Ability to perform a scientific sound simulation study including statistical analysis
- Ability to apply the algorithms from the course to combinatorial optimization problems
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Literature: | Slides completed by your own lecture notes . The following books are not mandatory but interesting for further reading: - The lectures on simulation are based on Simulation modeling and analysis, A.M. Law, McGraw-Hill Higher Education, 2015, ISBN 978-1-259-25438-3 (fifth edition) (you can also use an older edition).
- Integer Programming, Laurence A. Wolsey, Wiley-Interscience publication, 1998, ISBN 0-471-28366-5.
- The classic book: Computers and Intractability: A Guide to the Theory of NP-Completeness. M.R. Garey and D.S. Johnson, W.H. Freeman and Company, New York, 1979, ISBN 0-7167-1044-7.
- Algorithm Design. John Kleinberg, Eva Tardos, Pearson/Addision Wesley, 2005. ISBN 0-321-29535-8.
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Course form: | Lectures, self-study, exercises, assignments. | ||||||||||||||||||||||||||||||||

Exam form: | To pass the course the following is required:
- Simulation Assignment contributes 50 %
- Final written exam, minimal required grade 5.0, contributes 50 %
- Participation in mandatory sessions and meetings (indicated in the week schedule)
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Minimum effort to qualify for 2nd chance exam: | Participation in mandatory sessions and meetings (indicated in the week schedule) is required for additional examination. You can participate in additional testing for at most one part. If you have a reasonable chance of passing the course by the additional written exam, you have to take this opportunity. Additional testing in the Simuation Assignment, always requires permission of the teacher. |