Optimizing Load Balanced 3d-Bin Packing with Product Family: A Multi-Objective Genetic Algorithm

Abstract

Logistics companies continuously look for advanced methods to improve operational efficiency, and the problem of three-dimensional bin packing poses a significant challenge due to its complexity and practical relevance. This study proposes a novel hybrid genetic algorithm designed to address three key objectives simultaneously: (i) minimizing the number of containers used, (ii) achieving load balance by reducing deviations from the ideal barycenter, and (iii) preserving family unity within containers. The proposed algorithm presents a comprehensive approach to three-dimensional bin packing problems, addressing not only the three-dimensional placement and non-overlapping constraints emphasized by conventional approaches but also integrating real-world considerations such as orientation, load balancing, stacking, and product family constraints. The efficiency and robustness of the proposed algorithm have been demonstrated by extensive computational experiments and comparative analyses. The results highlight its potential to support more efficient and constraint-aware container loading strategies in real-world logistics operations.