Deep Learning in State of the Art Airline Crew Rostering Algorithms
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Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Nillius, Jonathan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
When distributing work among employees in Airline crew planning a problem
called the crew rostering problem is formed. It is a combinatorial optimization
problem and solving large problem instances commonly utilize column generation.
This thesis investigates utilizing machine learning predictions instead of reduced
costs in the pricing problem. The machine learning model predicts how likely it is
that a task is assigned a crew in a supervised learning fashion, by being trained on
historical planning problems. The aim is to then utilize the model to improve
computational speed in solving future problems.
This thesis presents results suggesting that it is conceptually possible to improve
computational time of state of the art crew rostering algorithms with accurate
predictions. Training a deep learning model able to make such accurate predictions
is found to be very difficult given the techniques and data experimented with.
Thus the thesis concludes that further research for improving this concept is
needed in two main directions, feature extraction and model techniques
Beskrivning
Ämne/nyckelord
airline crew rostering , machine learning , deep learning , combinatorial optimization , column generation , pricing problem , resource-constrained shortest path problem