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We study web and mobile applications that are used to schedule advanceservice, from medical appointments to restaurant reservations. We model them asonline weighted bipartite matching problems with non-stationary arrivals. Wepropose new algorithms with performance guarantees for this class of problems.Specifically, we show that the expected performance of our algorithms isbounded below by $1-\sqrt{\frac{2}{\pi}}\frac{1}{\sqrt{k}}+O(\frac{1}{k})$times that of an optimal offline algorithm, which knows all future informationupfront, where $k$ is the minimum capacity of a resource. This is the tightestknown lower bound. This performance analysis holds for any Poisson arrivalprocess. Our algorithms can also be applied to a number of related problems,including display ad allocation problems and revenue management problems foropaque products. We test the empirical performance of our algorithms againstseveral well-known heuristics by using appointment scheduling data from a majoracademic hospital system in New York City. The results show that the algorithmsexhibit the best performance among all the tested policies. In particular, ouralgorithms are $21\%$ more effective than the actual scheduling strategy usedin the hospital system according to our performance metric.

Online Advance Admission Scheduling for Services with Customer Preferences