Deep Learning in State of the Art Airline Crew Rostering Algorithms

Examensarbete för masterexamen

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Bibliografiska detaljer
Typ: Examensarbete för masterexamen
Titel: Deep Learning in State of the Art Airline Crew Rostering Algorithms
Författare: Nillius, Jonathan
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
Nyckelord: airline crew rostering;machine learning;deep learning;combinatorial optimization;column generation;pricing problem;resource-constrained shortest path problem
Utgivningsdatum: 2022
Utgivare: Chalmers tekniska högskola / Institutionen för data och informationsteknik
Samling:Examensarbeten för masterexamen // Master Theses

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