  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.
