We present a detailed analysis of the patient and resource scheduling problem in rehabilitation hospitals. In practice, the predominantly therapeutical treatments and activities which are prescribed for the patients are typically scheduled manually.This leads to rigid and inefficient schedules which can have negative effects on the quality of care and the patients' satisfaction. We outline the conceptual framework of a decision support system for the scheduling process that is based on formal optimization models. To this end, we first develop a large-scale monolithic optimization model. Then we derive a numerically tractable hierarchical model system in order to deal with problem instances of realistic sizes. We report numerical results with respect to solution times, model sizes and solution quality.

Suggested Citation: Helber, Stefan; Sahling, Florian; Schimmelpfeng, Katja (2011) AbstractWe present a stochastic version of the single-level, multi-product dynamic lotsizing problem subject to a capacity constraint. A production schedule has to be determined for random demand so that expected costs are minimized and a constraint based on a new backlog-oriented δ-service-level measure is met. This leads to a non-linear model that is approximated by two different linear models. In the first approximation, a scenario approach based on random samples is used. In the second approximation model, the expected values of physical inventory and backlog as functions of the cumulated production are approximated by piecewise linear functions. Both models can be solved to determine efficient, robust and stable production schedules in the presence of uncertain and dynamic demand. They lead to dynamic safety stocks that are endogenously coordinated with the production quantities. A numerical analysis based on a set of (artificial) problem instances is used to evaluate the relative performance of the two different approximation approaches. We furthermore show under which conditions precise demand forecasts are particularly useful from a production-scheduling perspective.

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