Appointment scheduling and the effects of customer congestion on service
Appointment scheduling and the effects of customer congestion on service
复制标题
预约安排以及客户拥堵对服务的影响
DOI：
10.1080/24725854.2018.1562590
复制
发表时间：
2019-10-03
影响因子：
2.6
通讯作者：
Xie, Xiaolan
中科院分区：
文献类型：
--
作者：
Zhang, Zheng;Berg, Bjorn P.;Xie, Xiaolan
摘要本文讨论了服务器对服务系统拥塞做出响应的预约调度问题。使用等待时间作为代理服务器的运行进度落后了多远，我们的特征作为客户的等待时间的函数的服务器的故障引起的行为。决策变量是特定客户序列的预定到达时间。我们的模型的目标是尽量减少客户的等待时间，服务器超时和服务器加速响应拥塞的加权成本。我们提供了替代配方这个问题作为一个模拟优化（SO）模型和随机可编程（SIP）模型，分别。我们表明，SIP模型可以解决中等规模的实例，正是在一定的假设下的服务器响应拥塞。我们进一步表明，SO模型实现了中等规模的问题，同时也能够扩展到更大的问题实例接近最优的解决方案。我们提出了这两种模型的理论结果，我们进行了一系列的实验，以说明最佳的时间表的特点，并衡量会计服务器响应拥塞时，安排预约的情况下，在一个大型医疗中心的门诊诊所的研究的重要性。最后，我们总结了从这项研究中获得的最重要的管理见解。
Abstract This article addresses an appointment scheduling problem in which the server responds to congestion of the service system. Using waiting time as a proxy for how far behind schedule the server is running, we characterize the congestion-induced behavior of the server as a function of a customer’s waiting time. Decision variables are the scheduled arrival times for a specific sequence of customers. The objective of our model is to minimize a weighted cost incurred for a customer’s waiting time, server overtime and server speedup in response to congestion. We provide alternative formulations of this problem as a Simulation Optimization (SO) model and a Stochastic Integer Programming (SIP) model, respectively. We show that the SIP model can solve moderate-sized instances exactly under certain assumptions about a servers response to congestion. We further show that the SO model achieves near-optimal solutions for moderate-sized problems while also being able to scale up to much larger problem instances. We present theoretical results for both models and we carry out a series of experiments to illustrate the characteristics of the optimal schedules and to measure the importance of accounting for a servers response to congestion when scheduling appointments using a case study for an outpatient clinic at a large medical center. Finally, we summarize the most important managerial insights obtained from this study.