Abstract
In this paper we address the problem of generating master surgical schedules (MSSs) that adhere to staff and equipment restrictions whilst ensuring patients are treated in a timely manner. We simultaneously address the master surgical scheduling problem (MSSP) and the surgical case assignment problem (SCAP). Stochastic surgical durations are considered in order to produce more robust schedules and reduce unexpected overtime. Also incorporated into the model are several constraints regarding patient wait targets that are set by the Australian government. The problem is formulated using a mixed integer nonlinear programming (MINLP) approach and solved using a variety of hybrid metaheuristics. The metaheuristics implemented are inspired by simulated annealing (SA) and reduced variable neighbourhood search (RVNS). In particular, we present an adaptive SA and hybridise SA and RVNS to greatly improve solution quality. The solution neighbourhoods used by the metaheuristics are based on the hierarchical structure present in the combined MSSP SCAP. We consider a case study of an Australian public hospital with a large surgical department and compare the performance of our model to historical data.
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