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Course module: 202001366
202001366
Stochastic Models
Course info
Course module202001366
Credits (ECTS)5
Course typeStudy Unit
Language of instructionEnglish
Contact persondr.ir. W.R.W. Scheinhardt
E-mailw.r.w.scheinhardt@utwente.nl
Lecturer(s)
PreviousNext 3
Lecturer
prof.dr. R.J. Boucherie
Lecturer
dr.ir. A. Braaksma
Lecturer
S. Dijkstra
Lecturer
J.W.M. Otten
Contactperson for the course
dr.ir. W.R.W. Scheinhardt
Academic year2020
Starting block
2B
RemarksPreferably, this module component needs to be followed in parallel with the component Project Stochastic Models.
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
Aims
After successful completion of this module component, the student is able to:
  1. formulate a Markov chain model for a given problem description and solve this model;
  2. formulate a Stochastic Dynamic Programming (SDP) model for a given problem description and solve this model;
  3. formulate a Markov Decision Process (MDP) model for a given problem description and solve this model;
  4. interpret the outcomes of an SDP and MDP model in order to construct an optimal strategy, which is applicable in a given practical situation;
  5. select an appropriate queueing model (M/M/1, M/M/c, etc.) for a given problem description and solve this model;
  6. interpret the implications of a queueing system’s performance on (given) performance indicators and formulate practical recommendations for system improvement.
Content
In the module component Stochastic Models, students first learn the basics of Markov chains (in discrete and continuous time, and Poisson processes). Using that knowledge, they next learn about stochastic dynamic programming and about queueing theory. Stochastic dynamic programming can be used to solve sequential multistage decision problems under uncertainty, e.g., transportation planning for multiple time periods with uncertain order arrivals and transportation times. Queueing theory can be used to analyse queueing problems that occur, e.g., in production and logistics, traffic, and communication networks.
Assumed previous knowledge
Probability Theory (from module 4)
Module
Module 8
Participating study
Bachelor Applied Mathematics
Required materials
Book
W.L. Winston, Operations Research, Applications and Algorithms (International Student Edition), Brooks/Cole - Thomson Learning, 4th edition (isbn 0534423620)
Recommended materials
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Instructional modes
Lecture

Q&A

Tutorial

Tests
Stochastic Models

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