English(EN) A Computational Method for Solving the Stochastic Joint Replenishment Problem in High Dimensions

深度神经网络解决高维库存问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

研究人员开发了一种新的基于仿真的计算方法，使用深度神经网络来解决高维随机联合补货问题。该方法将离散时间问题近似为连续时间脉冲控制问题，利用了与后向随机微分方程和随机目标问题的联系。所产生的可实施库存控制策略在高达50维的测试案例中，其性能已证明与现有基准相当或超越。 AI

排序理由这是一篇研究论文，详细介绍了一种针对特定优化问题的新型计算方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Bar{\i}\c{s} Ata, Wouter van Eekelen, Yuan Zhong · 2026-05-29 04:00

高维随机联合补货问题的一种计算方法

arXiv:2511.11830v2 Announce Type: replace-cross Abstract: We consider a discrete-time formulation for a class of high-dimensional stochastic joint replenishment problems. First, we approximate the problem by a continuous-time impulse control problem. Exploiting connections among …