Online Learning and Pricing for Service Systems with Reusable Resources
针对可重用资源（如云服务、租车）的收益管理问题，在需求和服务率未知的情况下，开发了在线学习算法并证明其最优遗憾界，帮助管理者在定价中边学边优化收益。
Revenue Management of Service Systems under Incomplete Information Revenue management with reusable resources finds many important applications in today's economy, such as cloud computing services, car/bicycle rental services, ride-hailing services, hotel management, project team management, and call center services. The existing literature predominantly assumes that the stochastic demand and service processes are given as an input to the models, and the pricing decisions are made with full knowledge of the distributional information. However, in practice, the decision maker may not know how demand or service rates react to price changes. Thus, the decision maker needs to learn the underlying mapping between prices and rates from past observations, while maximizing the total expected revenue on the fly. In “Online Learning and Pricing for Service Systems with Reusable Resources”, H. Jia, C. Shi, and S. Shen developed a series of online learning algorithms for revenue management problems with reusable resources and showed that they admit an optimal regret bound.