GOALI: Nurse Matching to Hospitals Using Static and Dynamic Allocation through an Online Platform

这个赠款机会学术联络与行业（GOALI）奖有助于通过支持技术的发展，以有效地匹配旅行和兼职护士空缺医院轮班使用在线平台，如由行业参与者操作的改善国民健康。为了充分有效，这样的在线平台必须考虑护士可用性，护士对轮班的偏好，护士不出现和公平考虑造成的不确定性。阐明这些问题会导致复杂的数学模型，需要开发解决方法。行业合作将为学者和从业人员之间建立新的沟通渠道，从而设计有效的匹配政策，以缓解护士短缺。伴随的教育计划将支持研究生和本科生教育，并为学生提供机会，开发操作方法，以解决重要的社会问题，同时获得与现实生活中的数据和行业参与者工作的实际经验。该研究项目将开发新的二进制和整数多项式优化模型的解决方案方法。该项目还将制定可实施的适应性政策的环境中，护士的供应和需求变得动态。将使用模拟以及行业合作伙伴提供的真实的数据来测试所开发技术的性能。这些数据也将用于估计护士偏好的功能表示，这将被纳入静态和动态优化和政策分析模型。该项目的成果也将有助于提高为自由职业者提供工作的在线平台的效率，或在服务经济中为客户提供商品和服务。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Grant Opportunity for Academic Liaison with Industry (GOALI) award contributes to the improvement of national health by supporting development of technology to efficiently match traveling and part-time nurses to vacant hospital shifts using online platforms such as that operated by the industry participant. To be fully effective such online platforms must consider uncertainty resulting from nurse availability, nurse preferences for the shifts, nurse no-show, and equity considerations. Incorporating these issues leads to complex mathematical models for which solution methodologies need to be developed. The industrial collaboration will create new channels of communications between academics and practitioners, leading to design of effective matching policies to alleviate nurse shortages. The accompanying educational plan will support graduate and undergraduate education and provide opportunities for students to develop operational methods to tackle an important societal problem, while gaining real-life experience working with real-life data and the industry participant.This research project will develop solution methods for novel binary and integer polynomial optimization models. The project will also develop implementable adaptive policies for environments where nurse supply and demand becomes available dynamically. The performance of developed techniques will be tested using simulations as well as real data provided by the industry partner. This data will also be used to estimate functional representation of nurse preferences, which will be incorporated in the static and dynamic optimization and policy analysis models. The outcomes from the project will also be useful in improving the efficiency of online platforms that offer jobs to freelancers, or offering goods and services to customers in a service economy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.