Aero-engine fleets scheduling optimization and task assignment under task-constraints

The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic, presenting a significant challenge in aviation engineering. This study proposes a Dual-layer Collaborative Optimization Framework (DCOF) to address these challenges. The problem is decomposed into two interrelated sub-problems, scheduling optimization and task assignment, with distinct mathematical models formulated for each. For scheduling optimization, this study proposes an Improved Gravity Particle Swarm Optimization (IGPSO) algorithm. The algorithm enhances global search capability and convergence speed through dynamic weight adjustment and constraint processing repair strategies, effectively handling dynamic variations in engine health and remaining life. For task assignment, an Improved Branch-and-Price (IB&P) method is used. This method combines column generation with branch-and-bound strategies, while integrating heuristic rules and parallel computing techniques to efficiently find optimal solutions under multi-dimensional constraints. By clearly distinguishing between operational and maintenance tasks and considering their interdependencies, the proposed DCOF better captures real operational needs, improving fleet scheduling efficiency and reliability. Experimental validation and engineering simulations confirm the method’s effectiveness, showing advantages in repair balance, task assignment balance, and minimizing engine life waste. The approach enhances both usage efficiency and maintenance management of aero-engine fleets.