Deep Learning in State of the Art Airline Crew Rostering Algorithms

Abstract

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