# Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling

## TL;DR

This paper introduces a learning-assisted large neighborhood search approach for vehicle routing in airport ground handling, effectively managing complex scheduling for numerous flights and operation types.

## Contribution

It develops a novel combination of imitation learning, graph convolutional networks, and large neighborhood search to improve vehicle routing optimization in airport ground handling.

## Key findings

- Handles up to 200 flights with 10 operation types
- Outperforms existing state-of-the-art methods
- Generalizes well to larger instances

## 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.