This study examines patient-initiated appointment rescheduling with consideration of patient preferences. Online rescheduling policies are investigated for the selection and sequential offering of new appointments upon the arrival of a rescheduling request via a telephone call. Appointments are offered until the patient accepts one or the maximum number of offers is reached. The aim is to reschedule appointments using a weighted function to maximise the patients’ satisfaction, optimise the operational performance, and minimise the number of patients deferred to a future time horizon. Different patient types are taken into account characterised by their uncertainties in rescheduling, cancellation, no-show, and service duration. The rescheduling process is formulated as a stochastic dynamic scheduling problem and approximated using a Markov Decision Process (MDP). Two heuristic policies are proposed, referred to as the myopic stochastic and the MDP-based algorithms. Both policies apply a simulation-optimisation approach that considers patient preferences and expected operational performance. To determine the set of offered appointments, the MDP-based algorithm additionally accounts for expected future rescheduling requests. Computational experiments are performed on real-life instances. The results demonstrate that the two proposed policies yield solutions of high quality. The myopic stochastic policy outperforms the MDP-based policy when it is challenging to offer suitable slots due to high capacity utilisation or a lack of clear patient preferences. Conversely, the MDP-based algorithm delivers better results when capacity utilisation is lower and there is some variation in preferences across days and patients.

中文翻译：

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根据患者偏好动态重新安排预约

本研究在考虑患者偏好的情况下检查了患者发起的预约重新安排。在线重新安排策略，以便在通过电话收到重新安排请求时选择和顺序提供新预约。在患者接受一个或达到最大报价数量之前，将提供预约。目的是使用加权函数重新安排预约，以最大限度地提高患者的满意度，优化运营绩效，并最大限度地减少推迟到未来时间范围的患者数量。考虑了不同的患者类型，其特征是他们在重新安排、取消、未出现和服务持续时间方面的不确定性。重新调度过程被表述为随机动态调度问题，并使用马尔可夫决策过程 （MDP） 进行近似。提出了两种启发式策略，称为近视随机算法和基于 MDP 的算法。这两项政策都采用模拟优化方法，该方法考虑了患者的偏好和预期的运营绩效。为了确定提供的预约集，基于 MDP 的算法还考虑了预期的未来重新安排请求。计算实验是在实际实例上执行的。结果表明，提出的两种策略产生了高质量的解决方案。当由于高容量利用率或缺乏明确的患者偏好而难以提供合适的时段时，近视随机策略优于基于 MDP 的策略。相反，当容量利用率较低并且不同日期和患者的偏好存在一些差异时，基于 MDP 的算法会提供更好的结果。