OPER 371 - Stochastic Processes & Queueing Models

The course is intended to provide the student with knowledge on stochastic processes and their applications.   Focus on discrete time Markov Chain with its definitions and popular applied examples.   The student will be able to define and classify the states of a given Markov chain. Student will be able to compute the long-run distribution of the Markov chain and its interpretation. Later, the student will be given a general knowledge on continuous time Markov Chain. Special focus on Poisson Process as an example of continuous-time Markov Chain. Finally, the course covers the birth and death processes as an introduction to queueing systems and modeling. The course covers number of Markovian Queues: single-server Markovian queue M/M/1, multi-server Markovian queue M/M/s, queueing models with zero buffer and some special types of queueing models.