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English(EN) GeoNest: Learning to Select Failure-Aware Neighborhoods for the Irregular Knapsack Problem in a Circular Container

GeoNest 框架通过强化学习提高包装效率

研究人员开发了 GeoNest,一个用于圆形容器内二维不规则背包问题的新型框架。该问题对于最大化制造业的材料利用率至关重要。GeoNest 采用通过强化学习训练的图策略来识别和选择多边形包装的最佳邻域,解决了残余空间被分割成无法使用的小口袋的问题。该框架在新基准 CircleNest-Bench 上得到证明,与现有的最先进求解器相比,平均利用率提高了约 0.6% 至 0.9%。 AI

影响 引入了一种新颖的强化学习方法来解决制造业中的复杂优化问题,有可能提高材料利用率。

排序理由 该集群描述了一篇关于优化问题新算法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

GeoNest 框架通过强化学习提高包装效率

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该集群描述了一篇关于优化问题新算法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhongman Du, Huiming Zhang, Linlin Yang, Sheng Xu, Baochang Zhang ·

    GeoNest:学习为圆形容器中的不规则背包问题选择故障感知邻域

    arXiv:2609.38863v1 Announce Type: cross Abstract: 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 produ…