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 Course module: 202000425
 202000425Stochastic Models
 Course info
Course module202000425
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)
 Previous 1-5 of 86-8 of 8 Next 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
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
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } After successful completion of this module component, the student is able to: formulate a Markov chain model for a given problem description and solve this model; formulate a Stochastic Dynamic Programming (SDP) model for a given problem description and solve this model; formulate a Markov Decision Process (MDP) model for a given problem description and solve this model; interpret the outcomes of an SDP and MDP model in order to construct an optimal strategy, which is applicable in a given practical situation; select an appropriate queueing model (M/M/1, M/M/c, etc.) for a given problem description and solve this model; interpret the implications of a queueing systemâ€™s performance on (given) performance indicators and formulate practical recommendations for system improvement.
 Content
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } 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
 1. Probability theory (e.g., random variables, cumulative distribution functions, probability density functions, conditional distributions and measures of a distribution).2. Operations research (e.g., basic knowledge of queueing models and understands the idea behind dynamic programming).
 Module
 Module 8
 Participating study
 Bachelor Industrial Engineering and Management
Required materials
Book
 W.L. Winston, Operations Research, Applications and Algorithms (International Student Edition), Brooks/Cole - Thomson Learning, 4th edition (isbn 0534423620) (IEM students already have this book in their possession)
Recommended materials
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Instructional modes
 Lecture Q&A Self study without assistance Tutorial
Tests
 Stochastic Models Exam
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