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

arXiv: https://arxiv.org/abs/2009.12501 · HTML: https://mlsanity.com/paper/2009.12501/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Structured Convolutional Kernel Networks for Airline Crew Scheduling](https://mlsanity.com/paper/2105.11646/) (2021)
- [Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes](https://mlsanity.com/paper/2202.12866/) (2022)
- [Imitation Learning for Combinatorial Optimisation under Uncertainty](https://mlsanity.com/paper/2601.05383/) (2026)

## Abstract (arXiv)

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.