Matching Impatient and Heterogeneous Demand and Supply
研究了Uber/Lyft等按需匹配平台在速度与匹配价值之间的权衡，发现等待时间需平衡收集足够代理与避免客户流失，且客户等待意愿的完整分布（不仅是均值）影响最优策略，并提出了匹配优先级排序算法。
Balancing Speed and Value in On-Demand Matching Platforms In “Matching Impatient and Heterogeneous Demand and Supply,” Aveklouris, DeValve, Stock, and Ward consider a fundamental trade-off faced by many platforms (e.g., Uber/Lyft) that match supply (e.g., drivers) and demand (e.g., riders) dynamically over time: making matches quickly capitalizes on the value of current supply and demand in the system, whereas waiting may enable better matches at the risk of losing impatient customers. They show that this trade-off can be balanced by waiting a short amount of time before making matches: long enough to gather enough agents to make valuable matches but not so long that impatient agents are likely to leave. Intuitively, this balance depends on how long agents are willing to wait, on average, but the authors show that it also depends on the full distribution of the willingness to wait (i.e., not only mean, but also variance and higher moments play a role). Thus, approaches that only take into account the mean willingness to wait may perform quite poorly. Further, the authors develop an algorithm to rank matching priorities in order to achieve an optimized trade-off between speed and value of matches.