arXiv cs.AI· Md Mahinur Alam, Kanita Jerin Tanha, Md Tanjin Alam, Shadman Sakib, Min Gyu Kim·· 2 小时前AI 评分27
AI 驱动的数字孪生框架优化仓库物流自动化
AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization
AI 导读
一项研究提出将强化学习智能体集成到基于 FlexSim 的数字孪生环境中，实现仓库货位实时动态优化，并通过 Asset Administration Shell 框架保证智能体、物理资产与数字孪生间的交互。仿真采用四区仓库布局和叉车搬运，结果显示平均拣货时间减少 29.66%，吞吐量提升 35.42%，行驶距离减少 18.54%。
正文 · 原文
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.
| Comments: | ICRA 2026 Workshop Advances and Challenges in AI-Driven Automation and Robotic System Integration with Digital Twins |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38101 [cs.AI] |
| (or arXiv:2610.38101v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38101 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Mahinur Alam [view email]
[v1]
Wed, 28 Oct 2026 00:37:21 UTC (1,079 KB)
来源：arXiv cs.AI · arxiv.org