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We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings.Keywords Health care · Patient scheduling · Multi-agent systems A preliminary version of this work has appeared as [1].

C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c aSoftware ENgineering

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Abstract. We consider the online problem of scheduling patients with urgencies and preferences on hospital resources with limited capacity. To solve this complex scheduling problem effectively we have to address the following sub problems: determining the allocation of capacity to patient groups, setting dynamic rules for exceptions to the allocation, ordering timeslots based on scheduling efficiency, and incorporating patient preferences over appointment times in the scheduling process. We present a scheduling approach with optimized parameter values that solves these issues simultaneously. In our experiments, we show how our approach outperforms standard scheduling benchmarks for a wide range of scenarios, and how we can efficiently trade-off scheduling performance and fulfilling patient preferences.