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保健物流中的研究，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，这严重影响了及时有效地提供基本医疗保健用品。尽管人们认识到无人机在这种情况下具有增强医疗保健服务的潜力，但考虑到环境因素（如天气和风况）及其对运营性能（如航程和有效载荷）的影响等实际限制因素，优化无人机操作已被证明具有挑战性。该合同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，同时考虑到天气条件的不确定性和每次交付的特定需求。该倡议不仅旨在提高农村地区的医疗保健可及性，而且还旨在推进医疗保健物流的最新水平，促进STEM领域的教育和多样性努力，并最终通过确保所有社区有效和公平的医疗保健可及性来支持国家健康和繁荣。本研究将开发一种决策支持工具，通过在可变天气和操作条件下战略性地使用无人机来改善农村医疗保健物流。概率能耗建模将允许将无人机性能的不确定性纳入稳健的优化模型，该模型可以在最坏情况下设计物流网络，同时优化可靠的操作，以促进包括无人机在内的多种运输方式之间的协作。将开发创新的解决方案算法来处理由于时间敏感需求而导致的复杂性，并将进行模拟实验来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大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该系统的整体有效性，检查操作效率和公平分配医疗用品之间的权衡。该奖项反映了美国国家科学基金会的法定使命，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.