Aircraft fleet readiness optimisation using reinforcement learning: a proof of concept.

A fleet of aircraft can be seen as a set of degrading systems that undergo variable loads as they fly missions and require maintenance throughout their lifetime. Optimal fleet management aims to maximise fleet availability and readiness while minimising overall maintenance costs. To achieve this goal, individual aircraft, with variable age and degradation paths, need to operate cooperatively to maintain high fleet availability while avoiding mechanical failure by scheduling preventive maintenance actions. Thereby, fleet management is a complex decisionmaking problem. In recent years, Reinforcement Learning (RL) has emerged as an effective method to optimise sequential decision-making problems (e.g., DeepMind's AlphaZero). In this work, we introduce an RL framework that can be employed to optimise the operation and maintenance of a fleet of aircraft. The operation of a fleet of aircraft is modelled in a simulated environment and Q-learning is employed to find the optimal policy. The RL solution is then evaluated against traditional operation/maintenance strategies and the results indicate that the RL policy performs relatively well over the fleet's lifetime. We conclude that RL has potential to help optimise and support fleet management problems. [ABSTRACT FROM AUTHOR]