Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
Abstract
We present a case study of using machine learning classification algorithms to initialize a large-scale commercial solver (GENCOL) based on column generation in the context of the airline crew pairing problem, where small savings of as little as 1% translate to increasing annual revenue by dozens of millions of dollars in a large airline. Under the imitation learning framework, we focus on the problem of predicting the next connecting flight of a crew, framed as a multiclass classification problem trained from historical data, and design an adapted neural network approach that achieves high accuracy (99.7% overall or 82.5% on harder instances). We demonstrate the usefulness of our approach by using simple heuristics to combine the flight-connection predictions to form initial crew-pairing clusters that can be fed in the GENCOL solver, yielding a 10x speed improvement and up to 0.2% cost saving.
Keywords
Cite
@article{arxiv.2009.12501,
title = {Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer},
author = {Yassine Yaakoubi and François Soumis and Simon Lacoste-Julien},
journal= {arXiv preprint arXiv:2009.12501},
year = {2021}
}
Comments
First publication on the "Cahiers du GERAD" series in April 2019