Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2023

Abstract

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.

Keywords

Airport ground handling, Airports, Atmospheric modeling, data-driven optimization, deep learning, Genetic algorithms, graph neural network, large neighborhood search, learning to optimize, Maintenance engineering, Optimization, Routing, Vehicle routing

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

35

Issue

9

First Page

9769

Last Page

9782

ISSN

1041-4347

Identifier

10.1109/TKDE.2023.3249799

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

http://doi.org/10.1109/TKDE.2023.3249799

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