A stochastic hierarchical approach for the master surgical scheduling problem

Abstract In a hospital, the Master Surgical Scheduling Problem involves assigning surgeons to time blocks in operating rooms over a planning horizon. This paper contributes mathematical models for this problem that consider multiple stakeholders and resources, including patients, operating rooms, surgeons, and recovery ward beds. The models meet external waiting time targets, minimize differences between the actual and target number of patients, maximize the utilization of operating rooms, minimize variations in the assignments of surgeons to both weekdays and operating rooms, minimize the total number of expected patients in the recovery ward, and minimize variations in the recovery ward utilization. In addition to exact methods, hybrid variable neighbourhood search - genetic algorithm and late acceptance hill climbing heuristics are used to obtain solutions. Rival models from the literature are compared to this approach. Results from numerical experiments show that it is possible to develop master surgical schedules that achieve these goals while outperforming the rival models.