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

EAGER：高级容量分配方法：拥塞服务系统中的时间敏感预约

• 批准号: 1548201
• 负责人: MARK VAN OYEN
• 金额: $ 24.21万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548201&HistoricalAwards=false
• 关键词: EAGER; Advanced; Capacity; Allocation; Methodology

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.

医疗保健和其他预约服务行业往往在漫长的服务就诊等待中苦苦挣扎。对不同服务提供商随时间推移访问患者的路径或路线进行建模变得越来越重要。现行的做法是在厘定预约日期时，强调先到先得的基本原则。这不能实现对相对紧急的患者给予相对较少等待的预约设置。这一早期概念探索性研究拨款(AGERGE)项目通过创建方法来弥补这一差距，这些方法允许组织为多种类型的患者提供服务，同时为每个组织提供预期，即他们的等待时间将接近某个适合患者紧急程度的目标水平(概率很高)。在共享服务资源的情况下，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，更好地协调不同提供者随着时间的推移对患者的护理，以及由于更及时的就诊而改善健康结果。这项工作将有助于扩大代表性不足群体在研究中的参与，并改进工程课程的内容。研究将解决上述围绕预约时间安排的挑战，重点是基于优化的资源分配规划模型。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。随着时间的推移，涉及多个服务类型的多次访问的护理行程将被处理，以增加模型的价值和相关性。该研究提出了包括混合整数规划优化方法在内的方法，该方法利用有效的线性近似来建模受控的多类排队网络。这些方法将估计性能度量，例如均值、方差和延迟约束违反概率。这些方法将在有限或无限的时间范围内优化接纳控制计划。该方法通过能够处理现实问题特征的新方法来解决上述挑战，这些特征包括但不限于(1)合并历史系统数据，(2)允许随时间变化的服务包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，例如等待时间和延迟约束违反概率的平均值和标准差。