Replenishment Decision

Replenishment decision-making aims to optimize inventory levels by balancing supply and demand, minimizing costs like waste and stockouts. Current research heavily utilizes reinforcement learning (RL), employing various architectures like multi-agent RL and deep policy iteration, often coupled with mathematical programming to handle complex constraints and large product catalogs. These advancements offer improved efficiency and cost savings compared to traditional methods, impacting supply chain management and potentially leading to more robust and adaptable inventory control systems across diverse industries.

Papers

October 28, 2024

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment
Yi Zheng, Zehao Li, Peng Jiang, Yijie Peng
Demand Prediction Dynamic Pricing DRL Algorithm Replenishment Decision

November 3, 2023

Using General Value Functions to Learn Domain-Backed Inventory Management Policies
Durgesh Kalwar, Omkar Shelke, Harshad Khadilkar
Reinforcement Learning Value Function Inventory Control Replenishment Decision Inventory Policy

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 12, 2023

Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
Massil Hihat, Stéphane Gaïffas, Guillaume Garrigos, Simon Bussy
Online Convex Optimization Online Algorithm Demand Prediction Inventory Management Inventory Control Replenishment Decision

March 19, 2023

Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX
Joseph Farrington, Kezhi Li, Wai Keong Wong, Martin Utley
Simulation Study Value Iteration Parallel Processing State of the Art JAX Operation Research Replenishment Decision

December 15, 2022

Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
Yuandong Ding, Mingxiao Feng, Guozi Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Houqiang Li, Yan Jin, Jiang Bian
Multi Agent Reinforcement Learning Stochastic Game Inventory Management PPO Algorithm Replenishment Decision

December 4, 2021

Deep Policy Iteration with Integer Programming for Inventory Management
Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi
Reinforcement Learning Policy Iteration Integer Programming Inventory Management Programmatic Reinforcement Learning Replenishment Decision

Replenishment Decision

Papers

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment

Using General Value Functions to Learn Domain-Backed Inventory Management Policies

MARLIM: Multi-Agent Reinforcement Learning for Inventory Management

Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization

Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX

Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management

Deep Policy Iteration with Integer Programming for Inventory Management