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Algorithms for decision support

Website:website containing additional information
Course code:INFOMADS
Credits:7.5 ECTS
Period:period 1 (week 36 through 45, i.e., 3-9-2018 through 9-11-2018; retake week 1)
Participants:up till now 82 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:Dit is een oud rooster!
lecture          Marjan van den Akker
tutorial group 1        Roel van der Broek
Lars van den Haak

The purpose of is to teach topics that:

  • 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.
It therefore contains a broad range of topics.

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:
  • 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: unrounded 5.
  • Participation in mandatory sessions and meetings (indicated in the week schedule)
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.
Minimum effort to qualify for 2nd chance exam: For additional testing on the assignment, explicit permission of the teacher is required.