题　目：Behavior-Aware Queueing: When Strategic Customers Meet Strategic Servers

嘉　宾：Yueyang Zhong, Ph.D. Candidate, University of Chicago

主持人：花成 助理教授 上海交通大学安泰经济与管理学院

时　间：2022年11月4日（周四）10:45-12:15pm

地　点：腾讯会议（校内师生如需获取会议号和密码，请于11月3日下午17点前发送电邮至managementscience@acem.sjtu.edu.cn）

内容简介:

Service system design is often informed by queueing theory. Traditional queueing theory assumes that customers are indefinitely patient and that servers work at constant speeds. However, when customers and servers in service systems are people, both systemic and monetary incentives created by design decisions influence their behavior. First, we use asymptotic analysis to study the behavior of strategic servers whose choice of work speed depends on managerial decisions regarding (i) how many servers to staff and (ii) how much to pay them, in the context of a finite-buffer many-server queue in which the work speeds emerge as the solution to a non-cooperative game. We then extend our model to also incorporate strategic customers' joining decisions. We analyze the behavior of joint equilibria in response to managerial decisions concerning (i), (ii), and (iii) how much to charge customers for service. In addition, we characterize the “price/benefit of anarchy” by comparing the performance under the equilibrium with that under the optimal solution to the centralized optimization problems.

演讲人简介：

Yueyang Zhong is a fifth-year PhD candidate in Operations Management at the University of Chicago Booth School of Business, advised by Prof. Amy Ward and Prof. John Birge. Previously, she received a bachelor’s degree in Industrial Engineering and Economics from Tsinghua University in 2018. Her primary research interests are in stochastic modeling and optimization of “modern” stochastic service systems with consideration of individual human behavior and imperfect systemic information. Her research has been recognized with several INFORMS paper competition awards, including a finalist in the IBM Service Science Best Student Paper Award (2022), and a finalist in the INFORMS Conference on Service Science Best Student Paper (2021).

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