This research aims to improve container loading strategies by optimizing three-dimensional bin packing with a focus on load balance and product family unity.
Developed a hybrid genetic algorithm to solve the 3D bin packing problem with multiple objectives.
Conducted extensive computational experiments to test the effectiveness of the proposed algorithm.
Compared results with conventional methods to assess efficiency and robustness.
The algorithm significantly reduces the number of containers used while achieving load balance and family unity in loading.
Computational experiments show improved efficiency over traditional approaches, with effective handling of orientation and stacking constraints.