Deep Learning in State of the Art Airline Crew Rostering Algorithms

Examensarbete för masterexamen

Please use this identifier to cite or link to this item:
Download file(s):
File Description SizeFormat 
CSE 22-05 Nillius.pdf2.71 MBAdobe PDFView/Open
Bibliographical item details
Type: Examensarbete för masterexamen
Title: Deep Learning in State of the Art Airline Crew Rostering Algorithms
Authors: Nillius, Jonathan
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
Keywords: airline crew rostering;machine learning;deep learning;combinatorial optimization;column generation;pricing problem;resource-constrained shortest path problem
Issue Date: 2022
Publisher: Chalmers tekniska högskola / Institutionen för data och informationsteknik
Collection:Examensarbeten för masterexamen // Master Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.