The work aims to propose an optimized solution that maximizes payload while ensuring load balance under practical loading constraints to deal with the cargo hold loading layout problem in aircraft. First, an optimization model that comprehensively considered multiple indicators, including payload capacity, center of gravity deviation, volume utilization, and moment of inertia, was constructed. Then, a hybrid solution strategy combining the Grey Wolf Optimizer and Genetic Algorithm was proposed. This algorithm deeply integrated the hierarchical search mechanism of Grey Wolf Optimizer with the evolutionary operations of Genetic Algorithm. A two-stage initialization strategy was employed to generate a high-quality initial population. The algorithm retained superior individuals through a combination of tournament selection and elite inheritance of the alpha wolf, and enhanced population diversity and solution feasibility via single-point crossover and a mutation operation based on the center-of-gravity distance. Meanwhile, convergence factors and mutation probabilities were dynamically adjusted to balance exploration and exploitation capabilities. Experiments were conducted using 20 test instances designed for the Boeing 777F freighter under complex constraints. Results showed that the proposed algorithm achieved an average reduction of approximately 28% in moment of inertia, a 42% average decrease in lateral imbalance, and a 91% reduction in computation time, while maintaining a high loading rate. The algorithm effectively addresses the three-dimensional loading problem in air cargo. The optimized layout demonstrates a stable "core-shell" structure, which promotes load centralization and flight stability. The research results validate the effectiveness and engineering applicability of the proposed method in the field of three-dimensional air cargo loading optimization.
Key words
air cargo /
3D loading /
grey wolf optimization algorithm /
genetic algorithm /
combined optimization
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