Online Advance Admission Scheduling for Services with Customer Preferences
Xinshang Wang, Van-Anh Truong, David Bank

TL;DR
This paper introduces new online algorithms for scheduling services with customer preferences, providing performance guarantees and demonstrating superior empirical results over existing heuristics in hospital appointment data.
Contribution
The paper develops the tightest known performance bounds for online weighted bipartite matching with non-stationary arrivals and applies these algorithms to real-world scheduling data.
Findings
Algorithms outperform existing heuristics in hospital scheduling data
Achieve at least 1-√(2/π)/√k performance ratio compared to offline optimal
Algorithms are 21% more effective than current hospital scheduling strategies
Abstract
We study web and mobile applications that are used to schedule advance service, from medical appointments to restaurant reservations. We model them as online weighted bipartite matching problems with non-stationary arrivals. We propose new algorithms with performance guarantees for this class of problems. Specifically, we show that the expected performance of our algorithms is bounded below by times that of an optimal offline algorithm, which knows all future information upfront, where is the minimum capacity of a resource. This is the tightest known lower bound. This performance analysis holds for any Poisson arrival process. Our algorithms can also be applied to a number of related problems, including display ad allocation problems and revenue management problems for opaque products. We test the empirical performance of our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptimization and Search Problems · Healthcare Operations and Scheduling Optimization · Supply Chain and Inventory Management
