Abstract: Integrated surgical scheduling refers to the coordination of various resources required for surgical patients, such as operating rooms, surgical staff, and hospital beds, during the scheduling process. The lack of intensive care resources, such as ICU beds equipped with monitoring equipment and staffed with medical personnel, poses a severe challenge to the healthcare system, such as delayed surgeries and postoperative health deterioration. The policies obtained using traditional surgical scheduling models often ignore the operating pressure of ICU resources. A bi-objective programming model was developed to simultaneously optimize the efficiency of the operating room and the operating pressure of the ICU. A modified genetic algorithm was proposed that enhances the flexibility of exploration by introducing diverse strategies to the mutation operator, and a repair operator was introduced to improve the feasibility of solutions under complex constraints. By employing a simulation-driven initialization approach, this study effectively overcomes the difficulty of generating a valid initial population in solving large-scale problems. Experiments based on real hospital data show that the algorithm obtains near-optimal solutions for small-scale arithmetic cases, and at the scale of 50 patients, the algorithm is able to reduce the maximum operating pressure of ICU by 10.00% to 28.56% compared to the surgery scheduling policy that only considers the operating room.

Key words: service scheduling, modified genetic algorithm, integrated scheduling, hospital operations management