题　目：Stall Economy: The Value of Mobility in Retail on Wheels

嘉　宾：曹隽羽，助理教授，德州大学奥斯汀分校商学院

主持人：曹宇峰，助理教授，上海交通大学安泰经济与管理学院

时　间：2022年12月14日（周三）8:30-10:00am

地　点：Zoom会议（校内师生如需会议号和密码，请发送邮件至yuxin.su@sjtu.edu.cn获取）

内容简介:

Urban open space emerges as a new territory to embrace retail innovations. Selling products in public spaces with wheeled stalls can potentially become ubiquitous in our future cities. Transition into such a "stall economy" paradigm is being spurred by the rapidly advancing self-driving technologies. Motivated by this transformation, this paper provides models, theory, and insights of spatial queueing systems in which one server moves around to meet mobile customers/machines and in which the "last 100 meters" is expensive. Specifically, we study two service modes i) on-demand, first-come-first-served, and ii) spatially and temporally pooling customer demands. In each mode, we derive the dependence of customer waiting and stall repositioning on two key decisions: the service zone size and the walking distance imposed on customers to meet a stall. In particular, for the on-demand mode, we propose and solve a "Rendezvous Problem" to analytically characterize the spatial distribution of the stall-customer meeting locations. We also propose a stylized joint truck-stall routing model to capture the inventory replenishment operations. Our main finding is that the stall economy potentially profits more than stationary retail, not only because of the mobility of stalls for providing proximity to customers, but also because of its operational flexibilities that allow for avoiding the "last 100 meters" and pooling demands. In a broader sense, this work looks toward an expanded scope of future retail empowered by self-driving technologies.

演讲人简介：

Junyu Cao is an assistant professor at McCombs School of Business, The University of Texas at Austin. Her research interests include data-driven decision making and stochastic modeling, with applications to smart-city operations and revenue management. She received her Ph.D. in the Department of Industrial Engineering and Operations Research (IEOR) from University of California, Berkeley in 2020.

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