Generating a stable primary schedule for an integrated surgical suite

Abstract

Efficient utilisation of operating room (OR) is a common anxiety of surgical suit manager which necessitate an effective planning and scheduling of surgeries. In this paper we investigate predictive/reactive scheduling of an integrated surgical suite in the form of a two-stage hybrid flow shop scheduling problem (HFSP). The deterministic model comprises both assignment and sequencing decisions for elective surgeries. By further considering shared capacity between elective and emergency patients, a chance constrained programming model is extended for the first time to cope with uncertain disruption. It is shown that how a chance constrained model will reduce to just considering an augmented surgery which processing time depends on distribution function of emergency surgery processing time and confidence level of scheduler. Two new important measures in reactive scheduling literature, 'stability' and 'robustness' are taking into account in surgical suite scheduling for the first time. Computational results demonstrate the efficiency of primary schedule generated by extended chance constrained programming model as well as the effectiveness of new measures in hospitals. As the chance constrained model is NP-hard, a decomposition heuristic algorithm based on tabu search (TS) is proposed to cope with problems of real size.