Patient Scheduling By Considering Uncertain Arrivals

Effective patient scheduling is important to improve patient satisfaction and healthcare efficiency.This research aims to determine the appropriate number of scheduled patients in each time period by considering nonscheduled patients' arrival in the Endoscopy Department of Longhua Hospital.The scheduled patients comprised four types with different examination times.It was assumed that nonscheduled patients arrive according to nonhomogeneous Poisson process and require immediate treatment.A stochastic integer programming model was formulated by assuming that there is a finite number of patients to be scheduled every working day.Waiting Time Target was introduced to ensure the service quality for nonscheduled patients and represented by the increasing weight of waiting times.The objective was to minimize scheduled and nonscheduled patients' waiting time,servers idle time,and overtime.Monte Carlo simulation was used to generate the random nonscheduled patients' arrival.Then,sample average approximation method was proposed to solve the stochastic programming model.A two-dimensional genetic algorithm with modified operators was applied to solve the problem.Sensitivity analysis was conducted to determine the impact of the parameters on the objective function.The numerical experiments showed that the proposed two-dimensional genetic algorithm is faster and more efficient compare to the traditional genetic algorithm.