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

TL;DR

This paper uses machine learning to predict airline crew connections, enabling better initial clustering for optimization, which results in significant speedups and cost savings in crew scheduling.

Contribution

It introduces a neural network-based prediction method for crew connections and demonstrates its effectiveness in improving optimization speed and cost efficiency.

Findings

Achieved 99.7% overall prediction accuracy

10x speed improvement in crew scheduling optimization

Up to 0.2% cost savings in airline crew planning

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.