MIE 360H: Systems Modelling and Simulation: This course is an introduction to modelling and analysis of complex stochastic systems using computer simulation. Simulation finds applications in various areas including manufacturing, service, healthcare, finance, and public policy. Broadly speaking, simulation models allow decision makers to evaluate systems that do not yet exist, and to answer what-if questions about them, e.g., How much will wait time on average be reduced if an emergency department adds another physician to the night shift? What is the expected increase in profit during the next quarter if a new investment strategy is employed? How many police patrol cars are required to ensure that the response time to a call is below 5 minutes, 90% of the time? The focus of this course will be on stochastic simulation and decision making under uncertainty. In particular, it covers topics termed Monte Carlo and Discrete-Event simulation.
Graduate
MIE 1605H: Stochastic Processes: Introduction to fundamental probabilistic models with emphasis on applications to queueing theory and service Engineering. Topics include discrete Markov chains, Poisson processes, Continuous-time Markov processes, renewal theory, Martingales, Brownian motion and Diffusion processes.
MIE 1613H: Stochastic Simulation: This course is a graduate level introduction to modelling and analysis of stochastic dynamical systems using computer simulation. The course provides a rigorous yet accessible treatment of the probability foundations of simulation, and covers programming simulation models in a lower-level language. Throughout the course, concepts and methods are illustrated using various examples from different application areas. In particular, applications to service and financial engineering are emphasized.
