神经网络机场地面处理
Neural Airport Ground Handling
摘要：
机场地勤为航班备降提供必要的操作保障，对提高机场管理效率和航空经济性具有重要意义。这样的问题涉及到操作之间的相互作用，导致具有复杂约束的NP-难问题。因此，现有的方法为AGH通常设计了大量的领域知识，但仍然无法有效地产生高质量的解决方案。在本文中，我们的目标是提高解决方案的质量和计算效率为解决AGH。特别是，我们首先模型AGH作为一个多车队车辆路径问题（VRP）与杂项约束，包括优先级，时间窗口，和能力。然后，我们提出了一个构建框架，将AGH分解为子问题（即，VRP）的车队，并提出了一种神经网络方法来构建这些子问题的路由解决方案。具体而言，我们采用深度学习，并使用经过强化学习训练的基于注意力的神经网络来参数化构造启发式策略，该神经网络在所有子问题中共享。大量的实验表明，我们的方法显着优于经典的元算法，构造算法和AGH的专门方法。此外，我们的经验验证，我们的神经方法推广到大量的航班或不同的参数的情况下，可以很容易地适应解决实时AGH随机航班到达。我们的代码可在www.example.com上公开获取。
英文摘要：
Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://github.com/RoyalSkye/AGH.
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。
