Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach

TL;DR

This paper introduces a novel RNN-inspired simulation method that significantly accelerates large-scale inventory optimization in complex production networks, enabling practical solutions where traditional models are computationally infeasible.

Contribution

The paper presents a new RNN-inspired simulation approach that leverages network structure to efficiently solve large-scale inventory optimization problems.

Findings

Simulation speed is thousands of times faster than existing methods.

Capable of handling large-scale networks with thousands of products.

Provides practical solutions within reasonable computational time.

Abstract

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.