报告内容摘要：

In this talk, we will argue that data-driven service systems engineering should take a statistical perspective that is guided by the decisions and performance measures that are critical from a managerial perspective. We further take the view that the statistical models will often be used as inputs to simulations that will be used to drive either capacity decisions or real-time decisions (such as dynamic staffing levels). We show that in dealing with high-intensity arrival streams (such as in the contexts of call center and ride sharing), the key statistical features of the traffic that must be captured for good performance prediction lie at much longer time scales than the inter-arrival times that are the usual focus of conventional statistical analysis for such problems. This observation is consistent with the extensive limit theory available for many-server systems. Our “top-down” approach focuses on data collected at these longer time scales, and on building statistical models that capture the key data features at this scale. In particular, we will discuss the use of Poisson auto-regressive processes as a basic tool in such “top-down” modeling, and on the statistical framework we are creating to build effective simulation-based decision tools based on real-world data.

报告人简介：

Zeyu Zheng is a PhD candidate in the Department of Management Science and Engineering at Stanford University.  His research lies at the interface of operations research and data sciences. Zeyu has done research on simulation, data-driven decision making, stochastic modeling, machine learning, and over-the-counter markets, and he has a PhD minor in Statistics and an MA in economics from Stanford University. Before coming to Stanford, he graduated from Peking University with a BS in mathematics.

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