Solving the aircraft engine maintenance scheduling problem using a multi-objective evolutionary algorithm

This paper investigates the use of a multi-objective genetic algorithm, MOEA, to solve the scheduling problem for aircraft engine maintenance. The problem is a combination of a modified job shop problem and a flow shop problem. The goal is to minimize the time needed to return engines to mission capable status and to minimize the associated cost by limiting the number of times an engine has to be taken from the active inventory for maintenance. Our preliminary results show that the chosen MOEA called GENMOP effectively converges toward better scheduling solutions and our innovative chromosome design effectively handles the maintenance prioritization of engines.