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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2020
- DOI:
- 10.48550/arXiv.2009.12501
- arXiv:
- arXiv:2009.12501
- Bibcode:
- 2020arXiv200912501Y
- Keywords:
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- Computer Science - Machine Learning;
- Mathematics - Optimization and Control;
- Statistics - Machine Learning
- E-Print:
- First publication on the "Cahiers du GERAD" series in April 2019