论文标题
大规模库存优化：经过重复的神经网络启发的模拟方法
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach
论文作者
论文摘要
许多大规模生产网络包括数千种类型的最终产品，以及数十万的原材料和中间产品。这些网络面临复杂的库存管理决策，对于库存模型而言，这些决策通常太复杂了，对于模拟模型来说太大了。在本文中，通过梳理复发性神经网络（RNN）的有效计算工具和生产网络的结构信息，我们提出了一种启发的RNN启发的模拟方法，该方法可能比现有的模拟方法快数千倍，并且能够在合理的时间内解决大型库存优化问题。
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.
