大规模库存问题Optimization：一种循环神经网络仿真方法
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach
摘要：
许多大型生产网络包括数千种最终产品和数万至数十万种原材料和中间产品。这些网络面临着复杂的库存管理决策，这往往是太复杂的库存模型和太大的仿真模型。本文结合递归神经网络（RNN）的高效计算工具和生产网络的结构信息，提出了一种基于RNN的模拟方法，该方法比现有的模拟方法快上千倍，能够在合理的时间内解决大规模库存最优化问题。
英文摘要：
Many large-scale production networks include thousands types of final products and tens to hundreds thousands types of raw materials and intermediate products. These networks face complicated inventory management decisions, which are often too complicated for inventory models and too large for simulation models. In this paper, by combing efficient computational tools of recurrent neural networks (RNN) and the structural information of production networks, we propose a RNN inspired simulation approach that may be thousands times faster than existing simulation approach and is capable of solving large-scale inventory optimization problems in a reasonable amount of time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
