The three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in operations research and logistics. Classical heuristics and constructive methods can generate packings quickly, but often fail to address industrial constraints such as stability, balance, and handling feasibility. Metaheuristics such as genetic algorithms (GAs) provide flexibility and the ability to optimize across multiple objectives; however, pure GA approaches frequently struggle with efficiency, parameter sensitivity, and scalability to industrial order sizes. This gap is especially evident when scaling to real-world pallet dimensions, where even state-of-the-art algorithms often fail to achieve robust, deployable solutions. We propose a KPI-driven GA-based pipeline for industrial 3D-BPP that integrates key performance indicators directly into a multi-objective fitness function. The methodology combines a layer-based chromosome representation with domain-specific operators and constructive heuristics to balance efficiency and feasibility. On the BED-BPP benchmark of 1,500 real-world orders, our Hybrid-GA pipeline consistently outperforms heuristic- and learning-based state-of-the-art methods, achieving up to 35% higher space utilization and 15 to 20% stronger surface support, with lower variance across orders. These improvements come at a modest runtime cost but remain feasible for batch-scale deployment, yielding stable, balanced, and space-efficient packings.

翻译：三维装箱问题（3D-BPP）是运筹学与物流领域长期存在的挑战。经典启发式与构造式方法虽能快速生成装箱方案，但往往难以满足稳定性、平衡性及操作可行性等工业约束。遗传算法等元启发式方法具有灵活性，并能实现多目标优化；然而，纯遗传算法常面临效率不足、参数敏感以及对工业订单规模的可扩展性有限等问题。当扩展至现实托盘尺寸时，这一缺陷尤为明显——即使当前最先进的算法也往往无法获得稳健且可部署的解决方案。本文提出一种面向工业三维装箱问题的KPI驱动遗传算法流程，将关键绩效指标直接整合至多目标适应度函数中。该方法结合基于层的染色体编码、领域专用算子与构造式启发式策略，以平衡效率与可行性。在包含1,500个真实订单的BED-BPP基准测试中，本文提出的混合遗传算法流程在各项指标上持续优于基于启发式与学习的最先进方法：空间利用率最高提升35%，表面支撑强度增强15%至20%，且在不同订单间表现出更低的方差。这些改进虽带来适度的运行时开销，但仍满足批量部署的可行性要求，最终生成稳定、平衡且空间高效的装箱方案。