A Hybrid Metaheuristic to Solving the Master Surgical Schedule under Downstream Beds Capacity

This paper addresses a decision tool to support elective surgery scheduling and planning problems. We propose a metaheuristic approach to solving the deterministic master surgical schedule (MSS) and the surgical case assignment problem (SCAP). We consider the capacity of the operating rooms (ORs) and the downstream resources, which involve the intensive care unit (ICU) beds and the post-surgery unit beds. The proposed approach considers OT restrictions and various resources availability (surgeons, OR, etc.). We built the MSS using an integer linear programming (ILP) model that minimizes the total assignment cost. Then, we propose an efficient genetic algorithm-based approach to overcome the large computation time generated by solving the SCAP. The patients are selected from the waiting list based on their due dates and clinical priority. Lastly, we propose a fast heuristic to manage the capacity of the downstream resources (ICU and post-surgery units’ beds). The computational experience is based on data provided from the archives of a hospital to compare the metaheuristic approach with the integrated ILP approach. The results demonstrate the efficiency of the proposed approach to solving the MSS and SCAP and the significant improvement obtained in the computation time.