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arXiv AI By Md Mahinur Alam, Kanita Jerin Tanha, Md Tanjin Alam, Shadman Sakib, Min Gyu Kim

AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization

Read the original on arXiv AI →

The paper introduces an AI‑driven digital twin framework that uses a reinforcement learning agent within a FlexSim simulation to optimize real‑time product placement in a four‑zone warehouse. By continuously learning from live operational data, the agent reduces picking time, increases throughput, and shortens travel distance. Experimental results show a 29.66% drop in average picking time, a 35.42% throughput gain, and an 18.54% travel distance reduction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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