Aircraft Assembly AO Based on Hybrid Intelligence Algorithms Multi-Process Reorganization and Optimization

To address the low efficiency in the Assembly Order (AO) preparation process of aircraft assembly wing-body docking station, characterized by an excessively long critical path and frequent resource conflicts, we propose an AO optimization framework integrating multi-process reorganization. High-frequency process combinations are mined from the historical AO data to construct a standardized process sequence library, enabling intelligent reorganization of the original AO. A hybrid approach integrating the Migration Hybrid Genetic-Particle Swarm Optimization algorithm (MHGAPSO) with the Critical Path Method (CPM) is employed to perform multi-objective optimization of the reorganized AO process, aiming to minimize the total critical path duration and balance resource utilization. The proposed method is validated by using 30 assembly cases of a specific aircraft assembly line. The results demonstrate that, compared with the conventional approaches, the AO restructuring time is reduced by 65%, the total critical path duration decreases by 15%, and the equipment load fluctuations are controlled within 20%. This work provides a scientific and practical basis for supporting the multi-batch aircraft assembly production.