题　目：Spatial Staffing

嘉　宾：胡明 教授 多伦多大学

主持人：许欢 教授 上海交通大学安泰经济与管理学院

时　间：2025年12月11日（周四）10:00-11:30

地　点：安泰楼A509室

内容简介：

We study the staffing problem for an on-demand vehicle-sharing platform operating over a d-dimensional service region. The platform makes a one-time capacity decision of how many vehicles to employ at the start of an infinite horizon, and dynamically controls vehicle-customer matching and routing over time. The objective is to minimize the long-run average cost, which includes vehicle operations costs and customer waiting costs. We show that the optimal staffing level consists of a nominal load (i.e., the minimum number of vehicles to ensure system stability) plus a safety buffer. This safety level depends on key system parameters, including the dimensionality d, the distributions of customer origins and destinations, and the ride-pooling capacity q (i.e., the maximum number of passengers per vehicle). Specifically, (i) when q=1, the safety level scales with the arrival rate raised to the power of d/(d+1), a result that holds broadly across distributional assumptions and mirrors earlier findings under stylized spatial models. (ii) When q>1, the scaling becomes 2d/(2d+1), which, to our knowledge, was not discovered in the past. Importantly, we derive these results by analyzing exact vehicle routing without relying on stylized assumptions and by developing provably near-optimal policies for general spatial matching settings beyond those in prior work.

演讲人简介：

Ming Hu is the Distinguished Professor of Business Operations and Analytics at the University of Toronto, a professor of operations management at the Rotman School of Management, and an Amazon Scholar. He currently serves as editor-in-chief of Naval Research Logistics and as an associate editor for Management Science, Operations Research, and Manufacturing & Service Operations Management. He is a former chair of the Revenue Management and Pricing Section at INFORMS.

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