In addressing the challenge of large-scale packing of strongly heterogeneous cargo in logistics loading, this paper proposes a three-dimensional container loading algorithm that integrates a block-building heuristic, an optimized placement strategy, and an enhanced genetic algorithm. The proposed approach accounts for six practical constraints inherent in logistics packaging and establishes an optimization model aimed at maximizing container space utilization while adhering to permissible center of gravity deviations. First, a block construction heuristic is employed to preprocess the cargo by aggregating items into larger blocks, thereby significantly reducing problem size and computational complexity. Subsequently, an improved placement strategy, combined with a genetic algorithm, is applied to identify an initial feasible packing layout. Finally, a simulated annealing operator is introduced to further refine the solution through local optimization, thereby obtaining an enhanced loading solutions with improved loading efficiency. Through comparative experiments with other leading algorithms using public datasets, the results demonstrate that the proposed algorithm performs on par with existing methods in solving the three-dimensional packing problem for strongly heterogeneous items while satisfying multiple constraints. It is capable of providing high-quality loading solutions for logistics enterprises and offers valuable insights for addressing more realistic container loading problems in future research.
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