Abstract
Simulation and simulation optimization have primarily been used for static system design problems based on long-run average performance measures. Control or policy-based optimization has been a weakness, because it requires a way to predict future behavior based on current state and time information. This work is a first step in that direction with a focus on congestion measures for queueing systems. The idea is to fit predictive models to dynamic sample paths of the system state from a detailed simulation. We propose a two-step method to dynamically predict the probability that the system state belongs to a certain subset and test the performance of this method on two examples.
Huiyin Ouyang
Assistant Professor of Operations Management
My research interests include stochastic modeling and analysis of service systems, healthcare operations, simulation analytics, and data-driven decision making.