  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.
