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

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.