A combined optimization-simulation approach to the master surgical scheduling problem

Abstract

This paper addresses the master surgical scheduling problem. First, we present a mixed integer
\nprogramming model. The model assumes that the cases in a hospital’s waiting list can be classified into
\nhomogeneous surgery groups based on the resources (e.g. operating room, post-surgical beds) that they
\nare expected to require. Hence, it produces a solution that indicates, for each day of the month and for
\neach time slot of the day, the number of cases to treat and the surgery group these cases must belong to.
\nThe model maximizes the patient throughput, takes into account the cases’ due dates and allows for con-
\ntrol of the waiting list. Secondly, we illustrate the results of a simulation study through which we test the
\nmodel solution’s robustness against the randomness of surgery duration and the length of stay. Finally,
\nwe present a combined optimization–simulation approach that allows us to fine tune the optimization
\nmodel to trade-off robustness and efficiency. Our study shows that, by planning surgery groups instead of
\nindividual cases and by combining optimization and simulation, it is possible to obtain schedules that are
\nboth robust and easy to implement. In addition, it shows that such a combined approach allows for the
\nperformance of more accurate scenario analyses. The results presented in this paper are based on real data
\nfrom the Meyer University Children’s Hospital in Florence, which is one of the most renowned children’s
\nhospitals in Italy.