Operations Research II

COURSE OBJECTIVE
To be introduced to the theory of stochastic processes and models that are important in EOR practice. To learn modeling techniques for
translating an EOR problem into an appropriate stochastic model. To learn how to apply optimization and simulation techniques for
performance analysis of stochastic systems.

COURSE CONTENT
This is an introductory course in stochastic models. It builds upon the basic course in probability theory and extends the theory of static
probability to dynamic stochastic processes. The course focuses on Poisson process, discrete-time and continuous-time Markov chains, with applications to queueing models, network models, risk analysis, reliability problems, etc It also discusses dynamic optimization and
stochastic simulation of these systems.

TEACHING METHODS
Combined lectures and tutorials

TYPE OF ASSESSMENT
Midterm and final exam