A robust multiobjective integrated master surgery schedule and surgical case assignment model at a publicly funded hospital

• Integrated master surgical schedule and surgical case assignment problems. • Uncertain surgery durations and emergency arrivals. • Novel robust optimization (RO) transformation framework. • Lp-metric methodology to solve the multi-objective integer linear programming model. • Analyze RO models to trade-off between variability and infeasibility. This paper considers an integrated operating room (OR) planning and advanced scheduling problem in a surgery department comprising several specialties that share a fixed number of ORs and post-surgery beds. It jointly considers the allocation of surgical specialties to OR blocks together with the assignment of the subsets of patients from each specialty’s waiting list to the OR blocks over a one-week planning horizon. By integrating the master surgical schedule problem (MSSP) and surgical case assignment problem (SCAP), we extend the stability of the scheduling process. We consider both uncertain surgery durations and emergency arrivals. The aim of this study is to maximize both patient service levels and hospital efficiency. A multi-objective integer linear programming (MOILP) model is developed to minimize patient waiting times as well as to maximize the OR utilization rates. An Lp-metric methodology is applied to solve the final integrated objective function. The MOILP is transformed into a robust optimization model using a transformation framework. In addition, we develop a two-stage stochastic integer linear programming model. The effectiveness of three models is compared and demonstrated through an extensive numerical experiment carried out on a large set of instances based on real data.