Multi-period adaptive fleet planning problem

with Approximate Dynamic Programming

Master thesis (2017)

Authors

L. Requeno García Aerospace Engineering

Contributors

Bruno F. Santos (supervisor 1)
R. Curran (supervisor 1)
E. van Kampen (supervisor 1)
Faculty
Aerospace Engineering
Copyright: © 2017 Laura Requeno García
More Info
expand_more

Published Date

17-07-2017

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Aerospace Engineering

Abstract

This MSc thesis presents a stochastic modelling approach to the multi-period airline fleet planning problem. Approximate Dynamic Programming (ADP) is used to model the impact of demand uncertainty on fleet decisions. The proposed ADP algorithm applies local value function approximations resulting from Gaussian kernel regressions to estimate future airline operating profits. A case study at a reference airline is used to show the effectiveness and practicability of the proposed approach. Optimal solutions are achieved for the deterministic case of the problem, while obtained policies excel the most-likely deterministic solution on stochastic versions.