# Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers

arXiv: https://arxiv.org/abs/2006.05608 · HTML: https://mlsanity.com/paper/2006.05608/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks](https://mlsanity.com/paper/2401.15299/) (2024)
- [Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks](https://mlsanity.com/paper/2411.08550/) (2024)
- [Deep Policy Iteration with Integer Programming for Inventory Management](https://mlsanity.com/paper/2112.02215/) (2021)

## Abstract (arXiv)

We propose a framework that uses deep neural networks (DNN) to optimize inventory decisions in complex multi-echelon supply chains. We first introduce pairwise modeling of general stochastic multi-echelon inventory optimization (SMEIO). Then, we present a framework which uses DNN agents to directly determine order-up-to levels between any adjacent pair of nodes in the supply chain. Our model considers a finite horizon and accounts for the initial inventory conditions. Our method is suitable for a wide variety of supply chain networks, including general topologies that may contain both assembly and distribution nodes, and systems with nonlinear cost structures. We first numerically demonstrate the effectiveness of the method by showing that its solutions are close to the optimal solutions for single-node and serial supply chain networks, for which exact methods are available. Then, we investigate more general supply chain networks and find that the proposed method performs better in terms of both objective function values and the number of interactions with the environment compared to alternate methods.