We use agent-based simulation to compare the performance of four scheduling policies in youth health care. The policies deploy push/pull and centralized/decentralized concepts. The simulation model represents an authentic business case and is parameterized with actual market data. The model incorporates, among other things, non-stationary Poisson arrival processes, reneging and return mechanisms, and care provider’s client preferences. We have identified that performance measurement in youth health care should not be focused on queue lengths alone, which is presently the case, but should include a case difficulty parameter as well. The simulation results, together with contextual data obtained from stakeholder interviews, indicate that a push strategy with a centralized queue suits the sector best, which is different from the current real-world situation. This policy ensures a higher level of fairness in treatment provision because the care providers are compelled to take their share in treating the difficult and economically less attractive cases. The complexity of the case cannot be captured by current queuing theory methods. Our simulation approach incorporates these complexities, which turn out to be relevant for the scheduling policy decision. We validate the model and strategies using real market data and field expert discussions.

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doi.org/10.1007/978-3-319-27343-3_10, hdl.handle.net/1765/83419

Giesen, E., Ketter, W., & Zuidwijk, R. (2015). Dynamic agent-based scheduling of treatments: Evidence from the dutch youth health care sector. doi:10.1007/978-3-319-27343-3_10