  Dispatching vehicle fleets to serve flights is a key task in airport ground
handling (AGH). Due to the notable growth of flights, it is challenging to
simultaneously schedule multiple types of operations (services) for a large
number of flights, where each type of operation is performed by one specific
vehicle fleet. To tackle this issue, we first represent the operation
scheduling as a complex vehicle routing problem and formulate it as a mixed
integer linear programming (MILP) model. Then given the graph representation of
the MILP model, we propose a learning assisted large neighborhood search (LNS)
method using data generated based on real scenarios, where we integrate
imitation learning and graph convolutional network (GCN) to learn a destroy
operator to automatically select variables, and employ an off-the-shelf solver
as the repair operator to reoptimize the selected variables. Experimental
results based on a real airport show that the proposed method allows for
handling up to 200 flights with 10 types of operations simultaneously, and
outperforms state-of-the-art methods. Moreover, the learned method performs
consistently accompanying different solvers, and generalizes well on larger
instances, verifying the versatility and scalability of our method.
