Objective To address the challenges in multi-sortie carrier-based aircraft support scheduling, including complex multi-type object relationships, frequent resource competition, and tight task dependencies, this study aims to develop a scheduling approach that balances cross-sortie resource coordination with local task optimization, while enhancing rapid adaptability and generalization in dynamic combat environments.

Method A meta-learning enhanced heterogeneous graph scheduling method (Meta-HGS) is proposed. A heterogeneous tripartite graph consisting of carrier-based aircraft, support tasks, and support stations is constructed, where a heterogeneous graph attention network is employed to model node types and relation types differentially. Features are aggregated across sortie-level, task-level, and resource-level granularities, enabling unified optimization of cross-sortie resource competition and task temporal constraints. To further enhance adaptability under dynamic task distributions, a meta-learning mechanism is incorporated, comprising a Meta-Critic network for cross-task value evaluation and a Task-Actor encoding network to extract task-specific policy representations. The Meta-HGS framework uses inner- and outer-loop parameter updates to achieve fast policy transfer and convergence across different tasks. Additionally, the HGAT explicitly models heterogeneous node interactions and meta-path-based neighbor aggregation, preserving semantic information of task-task dependencies and task-station assignments. This integrated approach allows the model to handle complex multi-type object relationships, frequent resource competition, and tightly coupled task dependencies, ensuring stable and efficient scheduling across diverse and dynamic operational scenarios.

Results In three different-scale simulation scenarios, Meta-HGS reduced the average completion time by approximately 5.4% compared to GA, PSO, DQN, and G-A2C, while maintaining advantages in responsiveness and solution accuracy. Completion times for small, medium, and large scales were 42.1, 68.4, and 95.3, outperforming other methods by 1.6%–12.3%. Average response time (ART) and scheduling time (AST) remained the lowest, with ART at 45.3, 78.5, 156.2, and AST at 128.5, 256.3, 512.8, exceeding learning-based methods. The gap to OR-Tools’ optimal solutions was only 1.5%–3.1%, demonstrating high precision and stability. Memory usage was slightly higher than heuristic methods but comparable to DQN and G-A2C, supporting overall performance gains. Results indicate that Meta-HGS balances efficiency, real-time performance, and accuracy, providing an effective and robust solution for multi-wave carrier aircraft support scheduling.

Conclusion The Meta-HGS-based scheduling method effectively captures multi-granularity heterogeneous relationships, significantly improves the efficiency and timeliness of multi-sortie carrier-based aircraft support scheduling, and demonstrates strong task transferability and environmental adaptability. This approach provides a generalizable technical pathway for intelligent scheduling in highly dynamic and strongly coupled support scenarios.