协调式多无人机最后一英里配送：学习面向节能与及时运营的策略
Coordinated Multi-Drone Last-mile Delivery: Learning Strategies for Energy-aware and Timely Operations
AI总结：
本文研究能量感知多无人机最后一英里配送问题，通过K-means聚类、强化学习和优化计划选择，并基于多智能体深度强化学习提出新算法，实现节能与及时配送。
摘要：
无人机近年来已成为包裹最后一英里配送中更快、更安全且成本效益更高的方式，尤其是在疫情期间凸显的紧急医疗配送中。本文针对由一群具有能量感知能力的无人机执行多包裹配送的新挑战，并考虑了对时间敏感的客户需求。每架无人机在其受电池限制的飞行范围内规划最优多包裹路线，以最小化配送延误并降低能耗。该问题通过分解为三个子问题来解决：（1）使用K-means聚类优化仓库位置和服务区域；（2）通过强化学习确定无人机的最优飞行范围；（3）通过一种新的优化计划选择方法规划并选择多包裹配送路线。为了整合这些解决方案并提升长期效率，我们提出了一种利用基于actor-critic的多智能体深度强化学习的新算法。使用真实配送数据集进行的大量实验证明了所提算法的卓越性能。我们为实际物流应用提供了关于经济效率（最小化能耗）、快速运营（减少配送延误和总体执行时间）以及仓库部署战略指导的新见解。
英文摘要：
Drones have recently emerged as a faster, safer, and cost-efficient way for last-mile deliveries of parcels, particularly for urgent medical deliveries highlighted during the pandemic. This paper addresses a new challenge of multi-parcel delivery with a swarm of energy-aware drones, accounting for time-sensitive customer requirements. Each drone plans an optimal multi-parcel route within its battery-restricted flight range to minimize delivery delays and reduce energy consumption. The problem is tackled by decomposing it into three sub-problems: (1) optimizing depot locations and service areas using K-means clustering; (2) determining the optimal flight range for drones through reinforcement learning; and (3) planning and selecting multi-parcel delivery routes via a new optimized plan selection approach. To integrate these solutions and enhance long-term efficiency, we propose a novel algorithm leveraging actor-critic-based multi-agent deep reinforcement learning. Extensive experimentation using realistic delivery datasets demonstrate an exceptional performance of the proposed algorithm. We provide new insights into economic efficiency (minimize energy consumption), rapid operations (reduce delivery delays and overall execution time), and strategic guidance on depot deployment for practical logistics applications.
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。
