We study a fundamental online resource allocation problem in service operations in which a heterogeneous stream of arrivals that vary in service times and rewards make service requests from a finite number of servers/providers. This is an online adversarial setting in which nothing more is known...

We consider a single-server scheduling problem given a fixed sequence of job arrivals with random no-shows and service durations. The joint probability distribution of the uncertain parameters is assumed to be ambiguous and only the support and first moments are known. We formulate a class of...

Joint online learning and resource allocation is a fundamental problem inherent in many applications. In this problem, an agent must allocate resources while adaptively learning the distributions of unknown parameters under delayed feedback. We introduce a general personalized framework that...