Fleet Planning Under Demand and Fuel Price Uncertainty Using Actor-Critic Reinforcement Learning

Fleet Planning Under Demand and Fuel Price Uncertainty Using Actor-Critic Reinforcement Learning

Geursen, Izaak (TU Delft Aerospace Engineering)

Lopes Dos Santos, Bruno (mentor)
Yorke-Smith, Neil (graduation committee)
de Croon, Guido (graduation committee)

Delft University of Technology

Aerospace Engineering | Air Transport and Operations

2021-01-28

Current state-of-the-art airline planning models are required to decrease models either in size or complexity due to computational limitations, limiting the
operational applicability to problems of representative sizes. Models return suboptimal solutions, especially when confronted with factors of uncertainty. Considering the growing interest in the application of Machine Learning techniques in the Operations Research domain, and the proven success in other fields such as robotics, this research investigates the applicability of these techniques for airline planning. An Advantage Actor-Critic (A2C) Reinforcement Learning agent is applied to the airline fleet planning problem. Because of the increased computational efficiency of using an A2C agent, the problem is increased in size and the highly volatile uncertainty in fuel price is implemented.
Conversion was achieved, and when evaluating the quality of the solutions compared to a deterministic model, the performance was very satisfactory. The A2C agent was able to outperform the deterministic model, with an increasing performance as more complexity was added to the problem. It was found that
the introduction of additional uncertainty has a major effect on the optimal actions, which the agent was able to adapt to adequately.

Airline Fleet Planning
Stochastic Optimization
Reinforcement Learning
Advantage Actor-Critic
Fuel Price Uncertainty

http://resolver.tudelft.nl/uuid:1eed4184-5c4a-4e88-b54d-8c9751f79ebf

Student theses

master thesis

© 2021 Izaak Geursen