Journal of Systems & Management ›› 2023, Vol. 32 ›› Issue (5): 927-938.DOI: 10.3969/j.issn.1005-2542.2023.05.006

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Outpatient Appointment Allocation and Scheduling Optimization for Unpunctual Patients in Multi-Doctor Scenario

JANG Bowen,SUN Xinyuan   

  1. School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,Liaoning,China
  • Received:2022-08-08 Revised:2022-12-11 Online:2023-09-28 Published:2023-09-28

多医生场景下面向不守时患者的预约分配与调度优化

姜博文,孙鑫源   

  1. 大连海事大学航运经济与管理学院,辽宁 大连 116026
  • 作者简介:姜博文 (1993-),男,副教授。研究方向为医疗运作服务管理。
  • 基金资助:

    国家自然科学基金资助项目(71901046)

Abstract:

Patients may not be strictly punctual to the appointment time, which causes disorder between arrival and service. This paper mainly focuses on the unpunctuality of patients considering stochastic factors, such as cancellation and uncertain service time. To ensure the appointment effectiveness and consecutive doctor service, it proposes a service order alternative rule depending on actual arrival times of two adjacent patients. Towards the multi-doctor appointment system, it formulates a mixed 0-1 stochastic programming model which is transformed based on sample scenarios. Aimed at the model difficulties in large scale and variable complexity, it designs an UP-FU algorithm to decompose the model into two sub-problems, which iteratively solves the allocation model and optimizes the appointment time. Numerical experiments show that as patients are more likely to be late, the arrival variability increases, the probability of canceling reservations increases, the variety of appointment interval is higher, the schedule is generally closer, the outpatient department allocates more patients consecutively to the same doctor rather than to different doctors alternately.

Key words: unpunctual arrival, appointment interval, appointment cancellation, stochastic optimization

摘要:

患者可能无法严格按照预约时间准时到达,造成到达顺序与服务顺序不一致的乱序情况。以患者不守时因素作为主要研究对象,同时考虑取消预约、服务时间不确定等随机因素,为兼顾预约有效性和医生工作连续性,提出允许相邻预约序列两位患者根据实际到达时间交换顺序的服务规则,在多医生场景下建立混合0-1随机规划模型。基于抽样情景对模型进行转化,针对模型规模大、变量复杂的难点,设计UP-FU算法将模型分解为两个子问题进行迭代求解,分别决策患者-医生分配和优化预约时间。实验结果表明,随着患者更倾向于迟到、到达差异性增大以及取消预约概率增加,预约间隔的波动性更强、预约时间总体更紧密,门诊将更多患者连续分配给同一位医生而不是交替分配给不同医生。

关键词: 不守时到达, 预约间隔, 取消预约, 随机优化

CLC Number: