General Information
Course Code | X_400336 |
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Credits | 6 EC |
Period | P1+2 |
Course Level | 400 |
Language of Tuition | English |
Faculty | Faculty of Science |
Course Coordinator | prof. dr. G.M. Koole |
Examiner | prof. dr. G.M. Koole |
Teaching Staff |
prof. dr. G.M. Koole |
Practical Information
You need to register for this course yourself
Last-minute registration is available for this course.
Teaching Methods | Lecture |
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Target audiences
This course is also available as:
Course Objective
After completing this course, the student can1. understand the applicability and the limitations of discrete- and
continuous-time Markov decision processes
2. can model real-world decision problems into the Markov decision
theoretic framework
3. can implement decision models to calculate the optimal decision rules
4. can mathematically prove properties of the optimal decision rules in
specific systems
5. can deal with modeling and implementation issues related to the curse
of dimensionality
6. can model problems with partial observations
Course Content
This course deals with the theory and algorithms for stochasticoptimization with an application to controlled stochastic systems (e.g.,
call center management, inventory control, optimal design of
communication networks). We discuss aspects of semi-Markov decision
theory and their applications in certain queueing systems and also
discuss parts of the related field reinforcement learning. In
programming assignments, students learn to implement optimization
algorithms and experiment with them. Programming is done in R.
Teaching Methods
Lectures.Method of Assessment
Programming exercises, final exam.Assignments: 50%
Exam: 50%
The weighted average needs to be 5.5 or higher.
Entry Requirements
Programming experienceLiterature
Lecture notes will be posted on Canvas.Target Audience
mBA, mBa-D, mMath, mSFM.Recommended background knowledge
Stochastic Modeling (X_400646) or equivalent courses on StochasticProcesses and Queueing Theory.