Arrival Data Modeling
Operational decision making in service systems often depends largely on the characterization of the random fluctuations involved. Exogenous arrivals represent a primary source of uncertainty and their stochastic behavior needs to be modeled carefully. Recent empirical studies demonstrate that the arrival data usually exhibits a dynamics that is substantially more sophisticated than the Poisson process model (and even the renewal process model), both of which are the underlying assumption of a wide variety of results in the queueing theory.
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.
