People Innovation Excellence

STOCHASTIC PROCESSES (4 Credits)

Learning Outcomes:

On successful completion of this course students will be able to: Design a system when randomness is significant; Describe the effect of variability into a system’s behavior and performance; Apply Markov Chains to various kinds of problems; Apply basic inventory models; Define key concepts in production flow (i.e. bottlenecks, line balancing, and Little’s Law); Use open and closed Jackson networks and maintain throughput in a closed Jackson network and compute corresponding WIP levels.

Topics:

  1. Probability and Random Variables;
  2. Discrete-Time Markov Chains;
  3. Poisson Process;
  4. Continuous-Time Markov Chains;
  5. Renewal Process;
  6. Queuing Theory;
  7. Reliability Theory.

 

 


Published at : Updated

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