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

医疗保健和其他以预约为基础的服务行业往往要忍受长时间的就诊等待。这是越来越重要的模型路径，或行程，病人访问随着时间的推移，从不同的服务提供者。目前的做法是在确定约会日期时强调基本的先到先得原则。这就不能使预约设置为相对较紧急的病人提供相对较少的等待时间。这个探索性研究的早期概念拨款（EAGER）项目通过创建方法来解决这一差距，该方法允许组织为多种类型的患者提供服务，同时为每个患者提供他们的等待时间将接近适合患者紧急情况的目标水平（高概率）的期望。在共享服务资源的设置中，创建的方法将优化涉及关键资源利用、员工加班、完成的服务数量和预约等待时间的权衡。这些新方法的潜在影响包括通过提高效率改善成本控制，随着时间的推移更好地协调不同提供者对患者的护理，以及由于更及时的就诊而改善的健康结果。这项工作将有助于扩大代表性不足的群体对研究的参与，并改善工程课程的内容。本研究将着重研究基于优化的资源分配规划模型，以解决上述围绕预约调度的挑战。该方法将优化多类随机排队网络模型的复杂准入控制策略。将处理涉及多种服务类型在一段时间内多次访问的护理行程，以增加模型的价值和相关性。该研究提出的方法包括混合整数规划优化方法，该方法利用有效的线性逼近来建模受控的多类排队网络。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时间范围内优化录取控制计划。该方法通过新方法解决了上述挑战，这些新方法可以处理现实问题的特征，包括但不限于：(1)历史系统数据的结合；(2)允许服务包和随机访问行程过程随时间变化的行程流时间度量模型；(3)关键绩效指标的线性近似，如等待时间和延迟约束违反概率的均值和标准差。

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.