Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach
This addresses inventory management problems for large-scale production networks, offering a significant speed improvement but is incremental as it builds on existing simulation and RNN tools.
The paper tackles the challenge of large-scale inventory optimization in production networks with thousands of products and materials, proposing a recurrent neural networks-inspired simulation approach that is thousands of times faster than existing methods.
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.