医疗无人机库存管理的马尔可夫决策过程方法
A Markov Decision Process Approach for Managing Medical Drone Deliveries
摘要：
我们考虑在无人机中心优化配送操作的问题，该无人机中心将无人机调度到不同的地理位置，从而产生对医疗用品的随机需求。无人机交付是一种创新方法，它带来了许多好处，例如低接触交付，从而减少了大流行病和疫苗可预防疾病的传播。虽然我们专注于这项工作的医疗用品交付，但无人机交付适用于许多其他物品，包括食品，邮政包裹和电子商务。在本文中，我们的目标是解决与不同地理位置的随机需求相关的无人机交付挑战。我们考虑与地理位置相关的不同类别的需求，这些需求需要不同的飞行范围，这与无人机电池中的电量直接相关。我们根据它们与无人机枢纽的距离对随机需求进行分类，使用马尔可夫决策过程对问题进行建模，并使用代表一家知名无人机送货公司的真实数据进行计算测试。我们解决了这个问题，使用强化学习方法，并显示其高性能相比，使用动态规划的精确解。最后，我们分析了结果，并为管理无人机枢纽运营提供了见解。
英文摘要：
We consider the problem of optimizing the distribution operations at a drone hub that dispatches drones to different geographic locations generating stochastic demands for medical supplies. Drone delivery is an innovative method that introduces many benefits, such as low-contact delivery, thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, drone delivery is suitable for many other items, including food, postal parcels, and e-commerce. In this paper, our goal is to address drone delivery challenges related to the stochastic demands of different geographic locations. We consider different classes of demand related to geographic locations that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
