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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., 5-9-2016 through 11-11-2016; retake week 1)
Timeslot:C
Participants:up till now 45 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
formgrouptimeweekroomteacher
lecture   Mon 13.15-15.0037-44 BBG-083 Marjan van den Akker
 
Mon 15.15-17.0044 RUPPERT-123
RUPPERT-125
RUPPERT-136
Thu 11.00-12.4536 DDW-1.30
37-44 BBG-023
tutorial group 1        Vincent Bonnet
 
Exam:
week: 45Mon 6-11-201717.00-20.00 uurroom: EDUC-THEATRON
week: 1Thu 4-1-20189.30-12.30 uurroom: BBG-023retake exam
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 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.
  • 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.
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)
NB: If the unrounded grade for the final exam is smaller than 5 or you missed one or more mandatory sessions without timely reporting of a good reason, your final grade can be at most 5. 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: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.
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