A Robust Optimization Approach For Elective Patients Scheduling

With the improvement of living standards and health awareness,the increase of the elderly population and the increasingly serious environmental problems,the demand for medical care is increasing rapidly.In the meantime,the demand of surgery is increasing rapidly,and the pressure on the hospital operating room is also increasing rapidly.However,at present,there is a lack of scientific and reasonable scheduling methods in most operating rooms in China,and manual scheduling is still used to arrange operations,which can easily lead to problems such as low utilization rate of operating room resources,overtime of medical staff,and cancellation of operation and so on.In the face of a large number of surgical demands,through scientific operating room scheduling,optimize the allocation of hospital limited surgical resources,improve the service quality and efficiency of the operating room,has become an important goal of operating room management.In the process of operating room scheduling,the constraints of resources should be fully taken into account.The lack of a certain resource can easily lead to the delay or cancellation of the operation.The multi-uncertainty of operating room scheduling mainly includes the uncertainty of the arrival of non-elective patients and the uncertainty of operation duration.In the hospital,the number of non-elective patients is far less than the number of elective surgery,so selective patient surgery is the main content of hospital operating room scheduling.The surgery duration,which is influenced by many factors,such as individual physical condition the surgeon skills and etc.is uncertain in advance.With uncertainty to scheduling surgeries is a very important and hard task for managers in hospital.Therefore,under the background of uncertainty duration of elective patients,this paper studies the scheduling problem of operating room considering the limitation of operating room and doctor's resources.First of all,from the point of hospital operating room and elective patients,the open cost of operating room and the punishment cost of patients waiting are quantified.We use minimizing the cost as the objective function,construct a deterministic model with fixed operation duration,considering the availability constraints of doctors and operating rooms and the scheduling rules of actual operating rooms.In the meantime,in order to reduce the negative influence of the uncertainty of operation time on operating room scheduling,the uncertainty of operation time is characterized by interval set,and the robust coefficient is introduced to control the conservatism of the robust model.A robust model of mixed integer programming with uncertain operation time is constructed and transformed into a robust equivalent model by duality theorem.According to the complexity of the problem and the characteristics of the model,combined with genetic algorithm and penalty function method,a hybrid genetic algorithm is designed to solve the problem.Based on the problem model,a hybrid vector group coding method is proposed,and a catastrophe strategy is proposed to increase the diversity of the population and prevent the population from falling into local optimization.Numerical simulation experiments are carried out with different fluctuation degrees and different robust coefficients,and the numerical simulation results verify the effectiveness of the proposed algorithm and robust optimization method.It is concluded that the scheduling scheme obtained by the robust equivalent model has good robustness,and the possibility of operation cancellation is effectively reduced at the same time.This paper enriches the research on the scheduling of operating room with uncertain operation time in theory,and provides a reference for the model construction and solution of the robust optimization problem of operating room scheduling for elective patients.The reference of operation scheduling scheme is provided for the operation scheduler to deal with the uncertainty of operation time.