|Website:||website containing additional information|
|Period:||period 1 (week 36 through 45, i.e., 2-9-2019 through 8-11-2019; retake week 2)
|Participants:||up till now 121 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|week: 45||Mon 4-11-2019||13.30-16.30 uur||room: EDUC-GAMMA|
|week: 2||Mon 6-1-2020||17.00-20.00 uur||room: EDUC-ALFA||retake exam|
|Contents:|| Due to illness, there is no lecture on Thursday September 5!!!
In data science we distinguish:
- Descriptive analysis: what is the current situation?
- Predictive analysis: what is going to happen in the future?
- Prescriptive analysis: which decision should I make?
This course and other courses in the field of algorithms focus on prescriptive analysis .
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 simulation and/or optimization algorithms. We focus on discrete models. You learn about computational complexity and about meaning of exact optimization algorithms, heuristics and what-if analysis.
For deterministic problems, we study well-known algorithms from combinatorial optimization. In particular, we study integer linear programming, since it is one of the most frequently applied techniques in practice. 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.
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:||The material of the course consists of:
- Simulation : slides, references to sections of the book by Law (copies of the most important section are available), exercises, and some solutions. There is also background material on statistics.
- Integer linear programming : slides, lecture notes, exercises, and solutions.
- Complexity: slides, exercises, and solutions.
Since there will be a lot of examples on the blackboard (especially on integer linear programming) it is strongly recommended that you make your own lecture notes .
For your learning experience, solutions to exercises will always be published some time later than the exercises. There will be solutions for a major part of (but not all) exercises.
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:
- Simulation assignment:
- completeness of the report, this means that it has to contain all the parts given in the workplan
- You attended the final feedback discussion in person. It is not sufficient if other members of your group attended.
- Final written exam: minimal required grade: unrounded 5.
|Minimum effort to qualify for 2nd chance exam:|| Additional examination :
- Minimum required effort:
- Your final grade is at least 4
- You received a pass for the milestones of the simulation assignment
- You attended every milestone meeting in person. It is not sufficient if other members of your group attended.
- Retake for at most one part (out of simulation assignment and exam):
- If exam < 5, then retake exam
- If exam < 6 and retake exam >= 6 would be sufficient, then retake exam
- Otherwise, either retake exam or repair assignment
- For repair assignment, permission of teacher required
- Maximum grade for repair assignment is 7
- If there are unforeseen extreme circumstances because of which you cannot make a milestone or a meeting, you have to notify the teacher beforehand by e-mail.