Civil aircraft maintenance interval optimization by ant colony Optimization algorithm

This study addresses the maintenance interval optimization problem for civil aircraft by proposing a multiobjective optimization model based on an improved ant colony algorithm, aiming to balance reliability, availability, and cost in maintenance strategies. Firstly, lifetime samples are extracted by integrating planned and unplanned maintenance data, and a Weibull distribution is employed to model failure characteristics. A multiobjective optimization framework is then established, incorporating reliability, availability, and cost as objective functions. An enhanced ant colony algorithm is introduced, leveraging pheromone concentration to guide search directions and dynamically adjust maintenance interval solutions, thereby generating a Pareto optimal solution set. Case studies on critical components of a specific aircraft validate the algorithm’s efficacy. The results demonstrate that this methodology provides theoretical support for dynamic maintenance interval adjustments in engineering practice. Future research should focus on optimizing algorithm parameters to further enhance computational efficiency.