ERI: Realistic Drone Integration in Rural Healthcare Supply Chains

该工程研究启动（ERI）奖支持将无人机技术整合到医疗物流中的研究，以解决农村地区获得医疗用品的挑战。农村社区受到基础设施有限、距离遥远、地形崎岖和恶劣天气条件的阻碍，严重影响了基本医疗用品的及时和有效交付。尽管无人机在这种环境中增强医疗保健提供的潜力已得到公认，但考虑到环境因素（例如，天气和风条件）及其对操作性能的影响（例如，范围和有效载荷）已被证明具有挑战性。该奖项通过开发和分析数学模型来满足这一需求，以支持无人机集成到多式联运网络中，同时考虑天气条件的不确定性和每次交付的具体需求。这一举措不仅旨在提高农村地区的医疗保健服务，而且还旨在推进医疗保健物流的最新发展，促进STEM的教育和多样性工作，并最终通过确保所有社区获得有效和公平的医疗保健来支持国家健康和繁荣。这项研究将制定一项决定-通过在多变的天气和操作条件下使用战略无人机来改善农村医疗后勤的支持工具。概率能耗建模将允许将无人机性能的不确定性纳入一个强大的优化模型，该模型在最坏情况下设计物流网络，同时优化可靠的运营，以促进包括无人机在内的多种运输模式之间的协作。将开发创新的解决方案算法，以处理由于时间敏感的需求造成的复杂性，并将进行模拟实验，以验证和验证基于无人机的交付系统的有效性。模拟将使用模拟农村景观的合成数据和与流行病供应链相关的真实数据进行设计。数值实验将通过比较使用和不使用无人机的情况来评估系统的整体有效性，检查操作效率和医疗用品公平分配之间的权衡。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 Engineering Research Initiation (ERI) award supports research enabling the integration of drone technology into healthcare logistics to address the challenges of access to medical supplies in rural areas. Rural communities are hindered by limited infrastructure, vast distances, rugged terrain and severe weather conditions, which significantly impact the timely and efficient delivery of essential healthcare supplies. Despite the recognized potential of drones to enhance healthcare delivery in such settings, optimizing drone operations considering practical constraints such as environmental factors (e.g., weather and wind conditions) and their impact on operational performance (e.g., range and payload) has proven challenging. This award addresses this need by developing and analyzing mathematical models to support drone integration into multi-modal transportation networks, considering the uncertainties of weather conditions and the specific needs of each delivery. This initiative aims not only to enhance healthcare access in rural areas but also to advance the state of the art in healthcare logistics, contribute to educational and diversity efforts in STEM, and ultimately support the national health and prosperity by ensuring efficient and equitable healthcare access across all communities.This research will develop a decision-support tool for improving rural healthcare logistics through strategic drone use given variable weather and operational conditions. Probabilistic energy consumption modeling will allow the incorporation of uncertainties in drone performance into a robust optimization model that designs the logistic network under worst-case scenarios while also optimizing reliable operations to facilitate collaboration among multiple transportation modes, including drones. Innovative solution algorithms will be developed to handle complexities due to time-sensitive demand, and simulation experiments will be conducted to verify and validate the effectiveness of the drone-based delivery system. The simulation will be designed using both synthetic data that mimics rural landscapes and real-world data related to pandemic supply chains. Numerical experiments will evaluate the system's overall effectiveness by comparing scenarios with and without drone use, examining the trade-offs between operational efficiency and equitable distribution of medical supplies.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.