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Found 3 papers, 1 papers with code
Structured Convolutional Kernel Networks for Airline Crew Scheduling
Motivated by the needs from an airline crew scheduling application, we introduce structured convolutional kernel networks (Struct-CKN), which combine CKNs from Mairal et al. (2014) in a structured prediction framework that supports constraints on the outputs.
Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation
The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings.
Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
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.
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