Ai agent optimizes warehouse layout to speed up picking and throughput
AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization
Artificial Intelligence
Summary
Warehouses traditionally arrange products in fixed spots, which is often inefficient when conditions change. The authors created a smart computer agent that learns from real-time data and adjusts where items are placed in a warehouse to make picking faster and increase overall productivity. They tested this in a detailed warehouse simulator with forklifts and found their method reduces the time to find items by almost 30 percent and makes the system work 35 percent faster. This approach uses a digital model of the warehouse that talks to the agent and real assets seamlessly.
What this means in practice
- •For warehouse operations teams: Improve product arrangement in warehouses dynamically to reduce picking times and increase throughput using AI-driven digital twins.
- •For industrial automation engineers: Integrate reinforcement learning agents with physical assets and digital twins for real-time logistics optimization in multi-zone warehouse environments.
- •For supply chain software vendors: Create commercial logistics management platforms that incorporate AI agents and digital twins to offer continuous warehouse slotting optimization services.$Commercial implications: This paper enables development of AI-enhanced warehouse software products that optimize slotting using real-time data and simulations.
Authors
Md Mahinur Alam, Kanita Jerin Tanha, Md Tanjin Alam, Shadman Sakib, Min Gyu Kim
Abstract
In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static product placement strategies are being progressively replaced by dynamic slotting approaches that can adapt to real-time variations in warehouse conditions. In this work, we present a novel framework that employs a reinforcement learning agent integrated within a FlexSim-based Digital Twin environment for real-time product placement optimization. The proposed agent continuously learns from streaming operational data, enabling it to reduce picking time while improving overall system throughput. The simulation environment models a four-zone warehouse layout with forklift-based material handling and leverages the Asset Administration Shell framework to ensure seamless interaction between the learning agent, physical asset, and the digital twin. Experimental evaluation demonstrates substantial performance gains, with the proposed approach achieving a 29.66\% reduction in average picking time, a 35.42\% increase in throughput, and an 18.54\% decrease in travel distance.