发表机构
Concordia University; Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)（康考迪亚大学; 企业网络、物流与运输跨校研究中心（CIRRELT））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对门诊预约调度中服务时间、守时性和出勤的不确定性，提出随机规划模型，无需采样即可处理患者和时间依赖分布，优于经典规则，显著降低成本。
AI 中文摘要
门诊预约调度必须在不确定的服务时间、守时性和出勤率下平衡提供者的空闲时间、加班时间和患者等待时间。这些因素可能遵循患者和时间依赖的概率分布，由于建模和计算复杂性，现有模型很少同时纳入这些因素。我们提出了一种随机规划模型，该模型无需采样即可用多项式数量的变量和约束捕获指数级多的情景。它适应患者依赖的服务时间以及患者和时间依赖的未到访和到达时间。我们还讨论了从电子健康记录中估计这些分布的方法。我们研究了个性化提醒以及因激励或负面诊所体验导致的出勤变化如何影响调度。我们的模型在合理的计算时间内最优地解决了多达14名患者的实例。其调度优于经典的Bailey型规则。即使是最佳变体的预期成本平均也高出97%。纳入患者依赖的服务时间平均降低了34%的总成本。考虑患者和时间依赖的不守时和未到访分别降低了12%和67%的成本。个性化提醒可降低23%的成本，敏感性分析表明这些收益在估计分布的中等误差下仍然稳固。这些发现表明，对个体患者行为建模和定制沟通可以减少等待、空闲时间和加班。
英文摘要
Outpatient appointment scheduling must balance provider idle time, overtime, and patient waiting under uncertain service times, punctuality, and attendance. These factors may follow patient-and-time-dependent probability distributions, which existing models rarely incorporate simultaneously because of modeling and computational complexity. We propose a stochastic programming model that captures exponentially many scenarios with polynomially many variables and constraints, without sampling. It accommodates patient-dependent service times and patient-and-time-dependent no-shows and arrival times. We also discuss estimating these distributions from electronic health records. We examine how personalized reminders and changes in attendance due to incentives or negative clinic experiences affect scheduling. Our model optimally solves instances of up to 14 patients in reasonable computational time. Its schedules outperform classical Bailey-type rules. Even the best-performing variant has 97% higher expected cost on average. Incorporating patient-dependent service times reduces total costs by 34% on average. Accounting for patient-and-time-dependent unpunctuality and no-shows reduces costs by 12% and 67%, respectively. Personalized reminders can reduce costs by 23%, and sensitivity analyses show that these gains withstand moderate errors in estimated distributions. These findings show how modeling individual patient behavior and tailoring communication can reduce waiting, idle time, and overtime.
