【主讲】张晓炜，香港科技大学大学助理教授

【主题】大型服务系统中的波动调整：统计，随机和模拟

【时间】2018年3月29日（周四）10:00-11:30

【地点】清华经管学院 伟伦楼385

【语言】英语

【主办】管理科学与工程系

【简历】张晓炜老师的简历

【Speaker】Xiaowei Zhang，Assistant Professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology

【Topic】Fluctuation Scaling in Large Service Systems: Statistics, Stochastics, and Simulation

【Time】Thursday, March 29, 10:00-11:30

【Venue】Room 385, Weilun Building, Tsinghua SEM

【Language】English

【Organizer】Department of Management Science and Engineering

【Abstract】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. In this talk, we will first argue that the conventional approach to arrival modeling which focuses on the microstructure, e.g., the distribution of the inter-arrival times, may be inadequate. Instead, as demonstrated via statistical experiments, the behavior of the arrival process over a longer time scale really matters for the system performance and operational decisions. Then, we will present a critical statistical feature 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.

【Bio】Xiaowei Zhang is an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. He received his Ph.D. in Management Science and Engineering in 2011 and M.S. in Financial Mathematics in 2010, both from Stanford University. His research interests include stochastic simulation, decision analytics, and applied probability.