发表机构
Beihang University; Communication University of China; Hangzhou Innovation Institute of Beihang University（北京航空航天大学; 中国传媒大学; 北京航空航天大学杭州创新研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对圆形容器中不规则背包问题的后期装箱瓶颈，提出基于强化学习图策略的故障感知大邻域搜索框架GeoNest，通过配对失败多边形与剩余口袋并诊断阻挡关系，在基准上平均提升利用率约0.9%。
AI 中文摘要
固定圆形容器中的二维不规则背包问题是一个重要的组合优化问题，旨在最大化制造中的材料利用率。传统的几何装箱求解器可以产生紧密排列的布局，但它们往往将剩余空间分割成孤立的、无法容纳有价值未放置多边形的小口袋。为了克服这一后期装箱瓶颈，我们提出了一种名为GeoNest的故障感知大邻域搜索框架，该框架由通过强化学习训练的图策略驱动。具体来说，我们首先通过将失败的目标多边形与剩余口袋配对来构建邻域。然后，我们使用解释性姿态来识别阻挡候选插入的已放置多边形。这些诊断出的阻挡关系为底层几何求解器定义了有界、固定物品的修复子问题。最后，图策略选择最有前景的子问题来执行。为了进行评估，我们引入了CircleNest-Bench，这是一个包含来自四个轮廓来源的2,391个负载控制实例的基准，包括一个留出的工业CAD来源。实验结果表明，在相同的总时间预算下，GeoNest在三个主要测试集上的平均利用率比最先进的独立装箱求解器提高了约0.9%，在留出的工业集上提高了约0.6%。
英文摘要
The two-dimensional irregular knapsack problem in a fixed circular container is an important combinatorial optimization problem for maximizing material utilization in manufacturing. Conventional geometric packing solvers can produce tightly packed layouts, yet they often partition the residual space into isolated small pockets that cannot fit valuable unplaced polygons. To overcome this late-stage packing bottleneck, we propose a failure-aware large neighborhood search framework named GeoNest, driven by a graph policy trained via reinforcement learning. Specifically, we first construct neighborhoods by pairing failed target polygons with residual pockets. We then use explanatory poses to identify the placed polygons that block candidate insertions. These diagnosed blocking relations define bounded, fixed-item repair subproblems for the underlying geometric solver. Finally, the graph policy selects the most promising subproblem for execution. For evaluation, we introduce CircleNest-Bench, a benchmark comprising 2,391 load-controlled instances from four contour sources, including a held-out industrial CAD source. Experimental results demonstrate that, under the same total time budget, GeoNest improves mean utilization over a state-of-the-art standalone packing solver by about 0.9% on average across the three main test sets and by about 0.6% on the held-out industrial set.
Comments9 pages, 3 figures
