Optimisation of Aircraft Maintenance Schedules

TL;DR

This paper explores the use of Evolutionary Algorithms to optimize aircraft maintenance schedules, aiming to efficiently assign staff and complete tasks within tight turnaround windows.

Contribution

It introduces an application of Evolutionary Algorithms to aircraft maintenance scheduling and benchmarks their performance on multiple problem instances.

Findings

Evolutionary Algorithms effectively generate high-quality maintenance schedules.

Benchmark results demonstrate the algorithm's suitability for the problem.

Representation and genetic operators are validated through extensive testing.

Abstract

We present an aircraft maintenance scheduling problem, which requires suitably qualified staff to be assigned to maintenance tasks on each aircraft. The tasks on each aircraft must be completed within a given turn around window so that the aircraft may resume revenue earning service. This paper presents an initial study based on the application of an Evolutionary Algorithm to the problem. Evolutionary Algorithms evolve a solution to a problem by evaluating many possible solutions, focusing the search on those solutions that are of a higher quality, as defined by a fitness function. In this paper, we benchmark the algorithm on 60 generated problem instances to demonstrate the underlying representation and associated genetic operators.