Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
By François Soumis, Simon Lacoste-Julien. 2019
10 citations recorded by the source corpus.
We present a case study of using machine learning classification algorithms to initialize a large scale commercial operations research solver (GENCOL) in the context of the airline crew pairing problem, where small savings of as little as 1% translate to increasing annual revenue by millions of dollars in a large airline. 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.
Subfields: Aerospace Engineering, Industrial and Manufacturing Engineering, Management Science and Operations Research, Evaluation & Benchmarks
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Citations in the corpus
- Structured Convolutional Kernel Networks for Airline Crew Scheduling (2021, methodology): “The results of training on the flight-connection dataset (Yaakoubi et al., 2019), a flight-based network structure modeled as a general conditional random field (CRF) graph, demonstrate that the proposed predictor is more suitable than other methods.”
- Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation (2020, background): “Previous work on weekly CPP [9] has shown that keeping only the 20 most probable outgoing arcs of each vertex leads to excellent solutions.”