Echelon Supply Chain

Echelon supply chains, encompassing multiple stages of production and distribution, present complex inventory management challenges. Current research focuses on optimizing inventory levels and production across these stages using advanced techniques like deep reinforcement learning (DRL), employing architectures such as radial basis function networks and neural additive models to improve upon traditional methods. These efforts aim to enhance efficiency, reduce costs, and improve responsiveness to fluctuating demand, particularly in the face of uncertainty, as demonstrated by comparisons against base-stock policies and linearized models. The resulting insights are valuable for both improving theoretical understanding of multi-echelon systems and informing practical supply chain management strategies.

Papers

January 29, 2024

A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management
Liqiang Cheng, Jun Luo, Weiwei Fan, Yidong Zhang, Yuan Li
Deep Reinforcement Learning Deep Q Network Inventory Management Echelon Supply Chain

August 3, 2023

MARLIM: Multi-Agent Reinforcement Learning for Inventory Management
Rémi Leluc, Elie Kadoche, Antoine Bertoncello, Sébastien Gourvénec
Reinforcement Learning Multi Agent Reinforcement Learning Inventory Management Inventory Control Echelon Supply Chain Replenishment Decision

July 22, 2023

Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Explainable AI Side Chain Supply Chain Book Review Decentralized Manner Echelon Supply Chain

March 18, 2023

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management
Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach
Reinforcement Learning Inventory Management Neural Additive Model Echelon Supply Chain

October 23, 2022

No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Mengxiao Zhang, Shi Chen, Haipeng Luo, Yingfei Wang
Online Optimization Online Learning Algorithm Inventory Management Demand Curve Echelon Supply Chain

April 20, 2022

Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains
Francesco Stranieri, Fabio Stella
Deep Reinforcement Learning Algorithm Echelon Supply Chain

February 25, 2022

Behaviorally Grounded Model-Based and Model Free Cost Reduction in a Simulated Multi-Echelon Supply Chain
James Paine
Model Free Deep Reinforcement Learning Order Model Echelon Supply Chain Minimal Cost Order Flow

January 12, 2022

Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning
Julio César Alves, Geraldo Robson Mateus
Deep Reinforcement Learning Markov Decision Process Sequential Decision Making Problem Nonlinear Programming Lead Time Echelon Supply Chain Demand Uncertainty

Echelon Supply Chain

Papers

A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management

MARLIM: Multi-Agent Reinforcement Learning for Inventory Management

Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution

Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains

Behaviorally Grounded Model-Based and Model Free Cost Reduction in a Simulated Multi-Echelon Supply Chain

Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning