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Transfer Learning-Enabled Multiobjective Optimization for Adjustable Aircraft Assembly Scheduling
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DOI：10.1109/TII.2025.3641258.png)
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Abstract
En 中文
Aircraft assembly scheduling (AAS) grows increasingly complex in the presence of multiple uncertainties, given that these factors significantly influence assembly process efficiency and the optimal allocation of resources. Existing scheduling methods have exhibited their limitations in taking into consideration of the uncertainties related to assembly resources, the actual number of workers that may change in process, and the previous assembly experience with high productivity. To overcome these limitations, a workstation adjustment mechanism (WAM) is proposed to improve the availabilities of workstations. WAM is fully integrated with actual worker configurations to mitigate the shortages of workers in the task conversions in a schedule. A knowledge transfer-based multiobjective evolutionary algorithm (KT-MOEA) is developed as a systematic optimization framework for addressing multiobjective AAS problems. Moreover, a design of experiments systematically evaluates the impact of controllable variables across multiple instances, which enhances the robustness, interpretability, and generalizability of the proposed framework. This innovative approach systematically integrates transfer learning and the nondominated sorting genetic algorithm II to enhance optimization performance. Quantitative results demonstrate that the proposed KT-MOEA significantly enhances optimization performance, achieving up to 50% improvement in convergence (inverted generational distance), substantial gains in solution diversity (hypervolume), and at least a 7% enhancement in the quality of Pareto-optimal solutions compared with benchmark algorithms.
Keywords:
Aircraft assembly scheduling (AAS)
knowledge transfer-based multiobjective evolutionary algorithms (KT-MOEA)
nondominated sorting genetic algorithm-II (NSGA-II)
workstation adjustment mechanism (WAM)
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