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arXiv · 2009.12501

Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer

Yassine Yaakoubi·François Soumis·Simon Lacoste-Julien
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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.

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Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien. 2021-03-02. Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer. https://arxiv.org/abs/2009.12501

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