
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.


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 knowledge1. 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). 
Bachelor Industrial Engineering and Management 
  Required materialsBookW.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 materialsInstructional modesLecture
 Q&A
 Self study without assistance
 Tutorial

 TestsStochastic Models Exam


 