动态预约调度
Dynamic Appointment Scheduling
AI总结：
本文研究可动态调整的预约调度问题，提出基于相位型分布和动态规划的方法，并通过数值实验证明动态调度优于静态调度且能纠正服务时间分布假设错误的影响。
摘要：
本文考虑在每次客户到达时，所有未来客户的日程安排都可以进行调整的预约调度问题。我们首先对服务时间服从指数分布的情形进行显式处理，然后开发一种基于相位型分布的方法，以覆盖服务时间平方变异系数不等于1的情形。该方法依赖于动态规划，状态信息包括等待的客户数量、正在接受服务的客户已服务的时间以及仍需安排的客户数量。通过一系列数值实验展示了动态调度的使用，实验表明：(i) 错误假设服务时间服从指数分布的影响，以及(ii) 通过重新调度（相对于静态调度）所获得的收益。
英文摘要：
This paper considers appointment scheduling in a setting in which at every client arrival the schedule of all future clients can be adapted. Starting our analysis with an explicit treatment of the case of exponentially distributed service times, we then develop a phase-type-based approach to also cover cases in which the service times' squared coefficient of variation differs from 1. The approach relies on dynamic programming, with the state information being the number of clients waiting, the elapsed service time of the client in service, and the number of clients still to be scheduled. The use of dynamic schedules is illustrated through a set of numerical experiments, showing (i) the effect of wrongly assuming exponentially distributed service times, and (ii) the gains (over static schedules, that is) achieved by rescheduling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
