Simulation Optimization
Modern stochastic systems often have a sophisticated structure. The system performance is generally not an analytical function of the decision variables, but a complex surface that can only be evaluated at discrete locations via noisy samples, usually from a simulation model. Since the sampling process is often expensive, we would like to identify the optimal decision with minimal samples. Simulation optimization is essentially a trade-off between exploitation, which tends to sample more at ‘‘promising’’ areas, and exploration, which tends to sample more at ‘‘uncharted’’ areas.
Xiaowei Zhang
Associate Professor
My research research focuses on methodological advances in stochastic simulation and optimization, decision analytics, and reinforcement learning, with applications in service operations management, financial technology, and digital economy.
