Zeyu Zheng - Top-Down Statistical Modeling

Seminar | February 16 | 2-3:30 p.m. | 3108 Etcheverry Hall

 Zeyu Zheng, Stanford University

 Industrial Engineering & Operations Research

Abstract: 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 start by discussing Poisson arrival modeling in the context of systems in which time-of-day effects play a significant role. We will discuss several new statistical tools that we have developed that significantly improve the quality of the performance predictions made by the simulation models. In the second part of our talk, we show that in dealing with high-intensity arrival streams (such as in the call center and the ride-sharing contexts), 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.

Short Bio: 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, data sciences, and decision making. 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.