The effectiveness of resource allocation under emergencies especially hurricane disasters is crucial. However, most researchers focus on emergency resource allocation in a ground transportation system. In this paper, we propose Learning-to-Dispatch (L2D), a reinforcement learning (RL) based air route dispatching system, that aims to add additional flights for hurricane evacuation while minimizing the airspace's complexity and air traffic controller's workload. Given a bipartite graph with weights that are learned from the historical flight data using RL in consideration of short- and long-term gains, we formulate the flight dispatch as an online maximum weight matching problem. Different from the conventional order dispatch problem, there is no actual or estimated index that can evaluate how the additional evacuation flights influence the air traffic complexity. Then we propose a multivariate reward function in the learning phase and compare it with other univariate reward designs to show its superior performance. The experiments using the real-world dataset for Hurricane Irma demonstrate the efficacy and efficiency of our proposed schema.

中文翻译：

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Learning-to-Dispatch：紧急情况下基于强化学习的飞行计划

在紧急情况下，尤其是飓风灾害下，资源分配的有效性至关重要。然而，大多数研究人员专注于地面交通系统中的应急资源分配。在本文中，我们提出了 Learning-to-Dispatch (L2D)，一种基于强化学习 (RL) 的航线调度系统，旨在增加额外的飓风疏散航班，同时最大限度地减少空域的复杂性和空中交通管制员的工作量。考虑到短期和长期收益，考虑到使用 RL 从历史航班数据中学习到的权重二分图，我们将航班调度公式化为在线最大权重匹配问题。不同于传统的订单调度问题，没有实际或估计的指标可以评估额外的疏散航班如何影响空中交通复杂性。然后我们在学习阶段提出了一个多元奖励函数，并将其与其他单变量奖励设计进行比较，以展示其优越的性能。使用飓风艾玛真实世界数据集的实验证明了我们提出的模式的有效性和效率。