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.
Health care, Patient scheduling, Multi-agent systems
Distributed Artificial Intelligence (acm I.2.11), ARTIFICIAL INTELLIGENCE (acm I.2)
Artificial intelligence (msc 68Txx)
Software (theme 1), Logistics (theme 3), Energy (theme 4)
Service Oriented Computing and Applications
Medical Information Agent
Intelligent and autonomous systems

Vermeulen, I.B, Bohte, S.M, Somefun, D.J.A, & La Poutré, J.A. (2007). Multi-agent Pareto appointment exchanging in hospital patient scheduling. Service Oriented Computing and Applications, 1(3), 185–196.