The two-dimensional irregular bin packing problem (2DIBPP) aims to pack a given set of irregular polygons, referred to as pieces, into fixed-size rectangular bins without overlap, while maximizing bin utilization. Although numerous metaheuristic algorithms have been proposed for the 2DIBPP, many industrial applications favor simpler constructive heuristics due to their deterministic behavior and low computational overhead. Among such methods, the DJD algorithm proposed by L'opez-Camacho et al. is one of the most competitive constructive heuristics for the 2DIBPP. However, DJD is less effective for cutting instances, in which many pieces can be seamlessly combined into larger polygons. To address the issue, we propose MergeDJD, a novel constructive algorithm that integrates and extends the DJD framework. MergeDJD first preprocesses the instance by iteratively identifying groups of pieces that can be combined into larger and more regular piece. It then employs an improved version of DJD, in which the placement strategy is enhanced to better handle non-convex and combined shapes, to pack all resulting pieces into bins. Computational experiments on 1,089 well-known benchmark instances show that MergeDJD consistently outperforms DJD on 1,083 instances while maintaining short runtimes. Notably, MergeDJD attains new best known values on 515 instances. Ablation studies further confirm the effectiveness of the proposed components. To facilitate reproducibility and future research, we have open-sourced the complete implementation and provided interfaces for visualizing packing results.

翻译：二维不规则装箱问题（2DIBPP）旨在将一组不规则多边形（称为部件）无重叠地装入固定尺寸的矩形箱中，同时最大化装箱利用率。尽管已有大量元启发式算法被提出用于求解2DIBPP，但许多工业应用因其确定性和低计算开销而更倾向于简单的构造式启发式算法。在此类方法中，L'opez-Camacho等人提出的DJD算法是2DIBPP最具竞争力的构造式启发式算法之一。然而，DJD在处理切割实例时效果欠佳，此类实例中许多部件可无缝组合成更大的多边形。为解决该问题，我们提出MergeDJD——一种集成并扩展DJD框架的新型构造算法。MergeDJD首先通过迭代识别可组合成更大且更规则部件的组对实例进行预处理，随后采用改进版DJD（其放置策略经过增强以更好地处理非凸及组合形状）将所有生成的部件装入箱中。在1,089个经典基准实例上的计算实验表明，MergeDJD在1,083个实例上持续优于DJD，同时保持较短的运行时间。值得注意的是，MergeDJD在515个实例上取得了新的已知最优值。消融研究进一步证实了所提出组件的有效性。为促进可复现性与未来研究，我们已开源完整实现并提供了装箱结果可视化接口。