We formulate a novel class of online matching problems with learning. In these problems, randomly arriving customers must be matched to perishable resources so as to maximize a total expected reward. The matching accounts for variations in rewards among different customer–resource pairings. It also accounts for the perishability of the resources. Our work is motivated by a healthcare application, but it can be easily extended to other service applications. Our work belongs to the online resource allocation streams in service systems. We propose the first online algorithm for contextual learning and resource allocation with perishable resources. Our algorithm explores and exploits in distinct interweaving phases. We prove that our algorithm achieves an expected regret per period that increases sub-linearly with the number of planning cycles.

我们精心设计了一类新颖的在线学习匹配问题。在这些问题中，随机到达的客户必须与易腐烂的资源匹配，以使总的预期回报最大化。匹配说明了不同客户资源对之间的奖励差异。它还说明了资源的易腐性。我们的工作受到医疗保健应用程序的启发，但可以轻松地扩展到其他服务应用程序。我们的工作属于服务系统中的在线资源分配流。我们提出了第一个在线算法，用于上下文学习和易腐资源的资源分配。我们的算法在不同的交织阶段中进行探索和利用。我们证明了我们的算法在每个周期都实现了预期的遗憾，该遗憾随着计划周期数的增加而呈次线性增加。