The growing demand for outpatient departments and prolonged waiting time of patients in recent years has made physician scheduling necessary to provide timely medical services. This study focused on the problem of scheduling physicians in an outpatient system with a nonhomogeneous patient arrival and priority queue, which exists in many Asian hospitals. In such a system, both patients with appointments and walk-in patients wait in a priority queue to see physicians, with the patients’ arrivals fluctuating throughout the day. In order to respond to time-related demands while simultaneously respecting physicians’ preferences for being on or off duty in some specific slots, a staffing optimization model was formulated. In addition, a physician rescheduling model was proposed for the case where a physician is unexpectedly absent. To solve the problem, a calibrated waiting time approximation-based genetic algorithm methodology was proposed. Its main contribution is the use of a data-driven analytical method to estimate the average waiting time of the two types of patients in a complex queuing system. The results of a numerical study and real case study in a Shanghai hospital showed that physician scheduling optimization on the basis of the proposed waiting time approximation method was effective and efficient when applied to the proposed outpatient system.

近年来，对门诊部门的需求不断增长，患者的等待时间延长，因此有必要安排医生以提供及时的医疗服务。这项研究的重点是在许多亚洲医院都存在的，患者到达和优先队列不均匀的门诊系统中安排医生的问题。在这样的系统中，有预约的病人和临时病人都在优先队列中等待看医生，病人的到来全天都在波动。为了响应与时间相关的需求，同时又尊重医生在某些特定时段上班或下班的偏好，制定了人员配备优化模型。另外，针对医生意外缺席的情况，提出了医生重新计划模型。为了解决这个问题 提出了一种基于校准等待时间近似的遗传算法。它的主要贡献是使用数据驱动的分析方法来估计复杂排队系统中两种类型患者的平均等待时间。在上海一家医院进行的数值研究和实际案例研究的结果表明，在所建议的门诊系统中，基于所建议的等待时间近似方法的医生调度优化是有效且高效的。