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We present a case study of using machine learning classification algorithmsto initialize a large-scale commercial solver (GENCOL) based on columngeneration in the context of the airline crew pairing problem, where smallsavings of as little as 1% translate to increasing annual revenue by dozens ofmillions 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, anddesign an adapted neural network approach that achieves high accuracy (99.7%overall or 82.5% on harder instances). We demonstrate the usefulness of ourapproach by using simple heuristics to combine the flight-connectionpredictions to form initial crew-pairing clusters that can be fed in the GENCOLsolver, yielding a 10x speed improvement and up to 0.2% cost saving.

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