|Website:||website containing additional information|
|Period:||period 1 (week 36 through 45, i.e., 4-9-2017 through 10-11-2017; retake week 1)
|Participants:||up till now 42 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|Teachers:||Dit is een oud rooster!
|week: 45||Mon 5-11-2018||13.30-16.30 uur||room: OLYMPOS-HAL1|
|week: 1||Thu 3-1-2019||9.00-12.00 uur||room: RUPPERT-C||retake exam|
The purpose of is to teach topics that:
It therefore contains a broad range of topics.
- are important for the working area of algorithms (in practice and theory)
- are prerequisites for other courses in the COSC program
- that are not encountered by all students in the bachelor.
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 discrete-event simulation . As assignment you have to perform a simulation study of the Uithoflijn, the new tram line that will connect Utrecht CS and the Uithof. For deterministic problems, we study well-known algorithms from combinatorial optimization .
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
|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.
- 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.
|Course form:||Lectures, self-study, exercises, assignments.|
|Exam form:||In the grading the simulation assignment contributes 50% and the written exam contributes 50%. To get a grade of at least 6 the following is required:
NB: If there are unforeseen extreme circumstances because of which you cannot attend a mandatory meeting, you have to notify the teacher beforehand by e-mail.
- Simulation assignment: completeness of the report, this means that it has to contain all the parts given in the workplan
- Final written exam: minimal required grade 5.0
- Participation in mandatory sessions and meetings (indicated in the week schedule)
|Minimum effort to qualify for 2nd chance exam:||You can participate in additional examination for at most one part out of
simulation assignment and written exam. For additional examination of the simulation assignment permission of the teacher is required.|