A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem with Capacity Constraints of Downstream Wards

Abstract

Operating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to cancellations. Thus, this study considers the operating room's capacity and downstream units while making surgery-related decisions. A mixed integer linear programming model for integrated planning consisting of two stages is proposed. The first stage model maximizes the scheduled surgical time of all operating rooms. In contrast, the second stage model aims to minimize the makespan of patients in operating rooms by incorporating sequence-dependent setup time and the capacity constraints of all resources under consideration at both stages. A two-stage genetic artificial bee colony algorithm (TGABC) hybrid of genetic algorithm and artificial bee colony algorithm is proposed to solve the model. Taguchi design of experiments is employed to fine-tune the parameters of the proposed TGABC algorithm. Experiments are designed to evaluate the performance of the proposed TGABC algorithm with generated instances mimicking real data for different-sized problems, and results are presented. The proposed method is compared with the exact method and three standard metaheuristics. It provides near-optimal results in comparatively shorter CPU time. Moreover, it outperforms the genetic algorithm, artificial bee colony algorithm, and simulated annealing in terms of solution quality compared on the considered test instances.