Constructing and evaluating a master surgery schedule using a service-level approach

Abstract

In this paper, we consider the problem to build a cyclic master surgery schedule for a case study of a small-to-medium-sized Belgian hospital. The problem considers the objectives of different stakeholders and aims to maximise the adjacency of operating room blocks assigned to an individual surgeon (group) and to optimise the bed capacity usage in the connected downstream units. To account for different sources of uncertainty surrounding the patient admissions and the required bed capacity in the downstream units, we formulate and solve a deterministic optimisation model relying on the service level concept to define higher-than-expected values for the patient demand. The yielded master surgery schedule establishes suitable bed capacity buffers in the downstream ward units, which improve the robustness to variations in the stochastic variables avoiding bed capacity overruns. The model is solved using a hierarchical two-stage procedure as a priority is established between the different objectives. In the first stage only the block adjacency objective is considered, whereas the second stage applies the ${\mathcal {E}}$-Constraint Method to find a set of solutions lying on or close to the Pareto front, making the trade-off between the usage of the bed capacity and the workload levelling in the hospital wards. The computational experiments provide insight in the (mutual) impact of the different considered objectives from a deterministic and stochastic point-of-view. We show that the scheduling process of surgeons can be improved by using an automated approach. The proposed method increased both the adjacency between OR blocks and the bed capacity usage significantly as all yielded solutions outperformed the master surgery schedule currently in use in the visited hospital. Findings have been derived using both a training and test real-life dataset to assess the resulting schedule robustness properly.