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Multi-agent deep reinforcement learning-based truck-drone collaborative routing with dynamic emergency response
Transportation Research Part E: Logistics and Transportation Review ( IF 8.3 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.tre.2025.103974
Wenhao Peng, Dujuan Wang, Yunqiang Yin, T.C.E. Cheng

In emergency disaster response, the dynamic nature and uncertainty of resource transportation pose significant challenges for vehicle routing planning. We address a truck-drone collaborative routing problem in humanitarian logistics, where a set of truck-drone tandems collaboratively deliver relief resources from a distribution center to a set of affected areas which is dynamically updated as disaster changes. In the truck-drone collaborative mode, as each truck performs the delivery services and serves as a mobile depot for the drone associated with it, the drone launches from its associated truck at a node, delivers relief resources to one affected area, and returns to rendezvous with the truck at the node or another node along the truck route. We cast the problem as a Markov game model with an event-driven method, which can effectively capture the dynamic changes in the states and node information of trucks and drones during relief resources delivery. To solve the model, we develop a multi-agent deep reinforcement learning algorithm, which combines prioritized experience replay and invalid action masking to improve the sample efficiency and reduce the decision space. We conduct extensive numerical studies to validate the effectiveness of the proposed method by comparing it with existing solution methods and two well-known heuristic rules, and discuss the impacts of some model parameters on the solution performance. We also assess the advantages of the truck-drone collaborative mode over the truck/helicopter-only mode through a case study of the 2008 Wenchuan earthquake.

中文翻译:


基于多智能体深度强化学习的货车无人机协同路线与动态应急响应



在紧急灾害响应中,资源运输的动态性质和不确定性对车辆路径规划提出了重大挑战。我们解决了人道主义物流中的卡车-无人机协作路线问题,其中一组卡车-无人机串联机协作将救援资源从配送中心运送到一组受灾区域,该区域会随着灾难的变化而动态更新。在卡车-无人机协作模式下,由于每辆卡车都执行配送服务并充当与其关联的无人机的移动仓库,无人机会在节点处从其关联的卡车起飞,将救援资源运送到一个受影响区域,然后返回在该节点或卡车路线沿线的另一个节点与卡车会合。我们将问题铸造为具有事件驱动方法的马尔可夫博弈模型,可以有效捕获救援资源运送过程中卡车和无人机的状态和节点信息的动态变化。为了解决该模型,我们开发了一种多智能体深度强化学习算法,该算法结合了优先经验重放和无效动作掩码,以提高样本效率并缩小决策空间。我们进行了广泛的数值研究,通过与现有的求解方法和两个众所周知的启发式规则进行比较来验证所提出的方法的有效性,并讨论了一些模型参数对求解性能的影响。我们还通过 2008 年汶川地震的案例研究评估了卡车-无人机协作模式相对于仅卡车/直升机模式的优势。
更新日期:2025-01-24
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