EAGER: Advanced Capacity Allocation Methodology: Time-sensitive Appointments in Congested Service Systems

医疗保健和其他以医疗保健为基础的服务行业往往需要长时间等待服务访问。随着时间的推移，从各种服务提供者那里模拟患者就诊的路径或行程变得越来越重要。目前的做法强调在确定任用日期时先到先得的基本规则。这不能实现将相对较少的等待给予相对更紧急的患者的预约设置。EARLY概念探索性研究补助金（EAGER）项目通过创建允许组织为多种类型的患者提供服务的方法来解决这一差距，同时为每个患者提供期望，即他们的等待时间将接近适合患者紧急情况的目标水平（具有高概率）。 在公共服务资源共享的环境中，所创建的方法将优化关键资源利用率、工作人员加班、完成的服务量和预约等待时间等方面的权衡。 这些新方法的潜在影响包括通过提高效率来改善成本控制，随着时间的推移，更好地协调患者在不同提供者之间的护理，以及由于更及时的访问而改善健康结果。这项工作将有助于扩大代表性不足的群体在研究中的参与，并改善工程课程的内容。这项研究将解决上述挑战周围的预约调度，重点是基于优化的规划模型资源分配。 该方法将优化多类随机排队网络模型的复杂接纳控制策略。行程的护理涉及多个服务类型随着时间的推移多次访问将被视为增加模型的价值和相关性。研究进展的方法，包括混合整数规划优化方法，利用有效的线性近似模型控制的多类神经网络。 该方法将估计性能指标，如均值，方差和延迟约束违反概率。该方法将在有限或无限的时间范围内优化接纳控制计划。该方法利用可以处理现实问题特征的新方法来解决上述挑战，所述现实问题特征包括但不限于（1）历史系统数据的并入，（2）允许随时间的服务捆绑和随机访问行程过程的行程流时间度量模型，以及（3）关键性能度量的线性近似，例如等待时间的平均值和标准偏差以及延迟约束违反概率。

Healthcare and other appointment-based service industries tend to struggle with long waits for service visits. It is increasingly important to model pathways, or itineraries, of patient visits over time from various service providers. Current practice emphasizes the basic first-come-first-served rule when setting appointment dates. This fails to enable appointment setting that gives relatively lesser waits to relatively more urgent patients. This EArly-concept Grant for Exploratory Research (EAGER) project addresses this gap by creating methods that allow organizations to service multiple classes of patient types while offering each the expectation that their waiting time will be close to some target level (with high probability) that is appropriate to the patient's urgency. In settings with service resources shared in common, the methods created will optimize trade-offs involving the utilization of key resource, staff overtime, volumes of services fulfilled, and waiting times for appointments. The potential impact of these new methods includes improved cost control through efficiency, better ability to coordinate a patient's care across providers over time, and improved health outcomes as a result of more timely visits. The work will help broaden participation of underrepresented groups in research and improve the content of engineering courses.The research will address the above challenges surrounding appointment scheduling with emphasis on optimization based planning models for resource allocation. The methods will optimize complex admission control policies for multi-class stochastic queueing network models. Itineraries of care involving multiple visits from multiple service types over time will be treated to increase the value and relevance of the models. The research advances approaches that include mixed integer programming optimization methods that exploit effective linear approximations to model controlled multi-class queueing networks. The approaches will estimate performance metrics such as means, variances, and delay constraint violation probabilities. The methods will optimize the admission control plans over a finite or an infinite time horizon. The approach addresses the above challenges with new methods which can treat realistic problem features that include, but are not limited to (1) the incorporation of historical system data, (2) itinerary flowtime metric models allowing for service bundles and stochastic visit itinerary processes over time, and (3) linear approximations of key performance metrics such as the means and standard deviations of waiting times and delay constraint violation probabilities.