Collaborative Research: Risk Management and Control of Dynamic Rate Queues for Healthcare and Transportation Services

合作研究：医疗保健和运输服务动态费率队列的风险管理和控制

• 批准号: 1436334
• 负责人: William Massey
• 金额: $ 20万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1436334&HistoricalAwards=false
• 关键词: Collaborative; Research; Risk; Management; Control

This research project addresses two critical national challenges: (1) cost reduction in healthcare. and (2) sustainable transportation. Both of these problems are addressed by leveraging the common modeling framework of waiting lines or queues that are sensitive to time of day, day of week, seasonal, or annual effects. In the healthcare domain, a solution to the nursing home bed planning problem can be developed. This is an important issue due to the aging baby boomer demographic. Also hospital bed cleaning costs can be lowered by finding the most effective time to have cleaning staff available. Drivers looking for parking generate a large amount of pollution and congestion. Methods, motivated by sustainable transportation and enabled by large data sets, to reduce the amount of pollution and congestion caused by parking can be developed. This research also includes the training of graduate students and the inclusion of underrepresented minorities. The Conference for African American Researchers in the Mathematical Sciences (CAARMS) came into existence in 1995 to nurture and promote African American researchers in the mathematical sciences. Both PIs have given lectures and tutorials at CAARMS to share with the community of African-American mathematicians and aspiring students some of their latest research work.This project develops dynamic performance models and dynamic optimization solution techniques based on a rich class of time dependent stochastic network models called Markovian service networks. This modeling and optimization framework captures the stochastic dynamics of the system that cannot be found in the static, long term, steady state behavior. Using this framework, solutions to critical challenges in both healthcare and sustainable transportation can be developed. These solutions address problems such as emergency room cost stabilization to handle the payments from the costs of unforeseen medical emergencies. The aging baby boomer demographic is the impetus for this work on nursing home bed planning. Hospital bed cleaning costs can be minimized by finding the most effective time to have cleaning staff available. On the average, 30% of the drivers in central business districts are searching for a parking space. This leads to a dramatic increase in air pollution and congestion. Using a big data set from a large scale parking pricing experiment, parking prices can be set to minimize pollution. This queueing analysis can also be used for a dynamic priority policy for electric car charging stations.

该研究项目解决了两个关键的国家挑战：（1）降低医疗保健成本。（2）可持续交通。 这两个问题都是通过利用对一天中的某个时间、一周中的某一天、季节性或年度影响敏感的排队或队列的通用建模框架来解决的。在医疗保健领域，可以开发针对养老院床位规划问题的解决方案。由于婴儿潮一代人口老龄化，这是一个重要问题。 此外，通过寻找最有效的时间配备清洁人员，可以降低医院床位清洁成本。寻找停车位的司机产生了大量的污染和拥堵。可以开发由可持续交通驱动并由大型数据集实现的方法，以减少停车造成的污染和拥堵。这项研究还包括培训研究生和纳入代表性不足的少数群体。非裔美国人数学科学研究人员会议（CAARMS）成立于1995年，旨在培养和促进非裔美国人数学科学研究人员。 两个PI在CAARMS的讲座和教程，与非洲裔美国数学家和有抱负的学生社区分享他们的一些最新研究工作。这个项目开发动态性能模型和动态优化解决方案技术的基础上，丰富的一类时间依赖的随机网络模型称为马尔可夫服务网络。这种建模和优化框架捕获了系统的随机动态，而这些动态在静态、长期、稳态行为中是找不到的。利用这一框架，可以开发出解决医疗保健和可持续交通领域关键挑战的解决方案。这些解决方案解决了诸如急诊室费用稳定等问题，以处理不可预见的医疗紧急情况的费用支付。老龄化的婴儿潮人口是推动这项工作的养老院床位规划。通过找到最有效的时间让清洁人员可用，可以最大限度地减少医院病床清洁成本。平均而言，在中央商务区，30%的司机正在寻找停车位。这导致空气污染和交通拥堵急剧增加。使用来自大规模停车定价实验的大数据集，可以设置停车价格以最大限度地减少污染。该决策分析也可用于电动汽车充电站的动态优先级策略。