Abstract
Operational decision making in service systems often depends largely on the characterization of the random fluctuations involved. Exogenous arrivals (e.g., customers, orders, etc.) represent a primary source of uncertainty and their stochastic behavior needs to be modeled carefully. In this talk, we will present a new statistical finding regarding the random fluctuations of the arrival process in large service systems, and propose a tractable model accordingly. When a service system under the new arrival model is scaled up, its dynamics is fundamentally different from that typical queueing analysis stipulates, and leads to a new staffing rule for managing the servers. At last, we will demonstrate via data-driven simulation that our staffing rule improves the system performance substantially in general.
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.