The three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in operations research and logistics. While classical heuristics and constructive methods can generate packings efficiently, they often fail to satisfy industrial requirements such as stability, balance, and handling feasibility. Metaheuristics such as genetic algorithms (GAs) offer greater flexibility, but pure GA approaches frequently struggle with efficiency, parameter sensitivity, and scalability to industrial order sizes. These limitations are particularly evident at real-world pallet dimensions, where even state-of-the-art methods often fail to produce robust, deployable solutions. We propose a KPI-guided GA-based pipeline for industrial 3D-BPP that integrates key performance indicators (KPIs) directly into a scalarized fitness function. The method combines a layer-based chromosome representation, domain-specific operators, and constructive heuristics to balance efficiency and feasibility. On the BED-BPP benchmark of 1,500 real-world orders, our GENPACK pipeline consistently outperforms heuristic and learning-based baselines, achieving up to 35% higher space utilization and 15-20% stronger surface support, while exhibiting lower variance across orders. These gains come at a modest runtime cost but remain practical 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。尽管经典启发式与构造方法能高效生成装箱方案，但其往往无法满足工业需求，如稳定性、平衡性及操作可行性。遗传算法等元启发式方法具有更强的灵活性，但纯遗传算法方法常面临效率低下、参数敏感性强及难以扩展至工业订单规模等难题。这些问题在真实托盘尺度下尤为突出——即便是最先进的方法也常无法生成稳健、可部署的解决方案。我们提出了一种基于KPI引导的遗传算法流水线，用于解决工业三维装箱问题，该方法将关键绩效指标直接整合至标量化适应度函数中。该技术结合了基于层的染色体表征、领域专用算子及构造式启发算法，以平衡效率与可行性。在包含1500份真实工业订单的BED-BPP基准测试中，我们的GENPACK流水线持续优于基于启发式与学习方法的基线方案，空间利用率最高提升35%，表面支撑强度增强15-20%，且订单间方差更低。这些性能提升虽带来适度的运行时间成本，但足以支持批量级实际部署，可生成稳定、平衡且空间高效的装箱方案。