OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items

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On September 7, 2026, arXiv cs.LG published the research results of OR-Transformer. This study proposed a new architecture aimed at expanding the processing capabilities of real-time decision-making systems to handle the scale of 1,000 items.

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Key entitiesKEY ENTITIES

OR-Transformer

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Keyword heat

OR-Transformer1

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A arXiv cs.LG en 2026-09-07 12:00

研究人员提出 OR-Transformer，这是一种用于随机需求下联合补货的深度学习框架。该框架采用物品排列等变 Transformer 架构及基于库存动态的路径梯度训练方法。在涵盖最多 1,024 个库存物品的规模测试中，OR-Transformer 的表现随规模增大而优于学习和滚动时域混合整数线性规划（MILP）基线模型。相比 MILP 求解器，该框架将在线决策时间缩短了超过 400 万倍，实现了供应链操作中大规模实时深度强化学习。