  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.
