用强化学习自动调度仓库任务，效率提升60%
Reinforcement Learning for Autonomous Warehouse Orchestration in SAP Logistics Execution: Redefining Supply Chain Agility
- 将仓库流程建模为动态环境，用强化学习实时优化任务分配
- 在30万条模拟数据上实现95%任务优化准确率，处理时间减少60%
- 适合关注供应链智能化、SAP系统集成的物流技术团队
在供应链需求不断增长的背景下，SAP Logistics Execution（LE）对管理仓库、运输和交付至关重要。本研究提出一种开创性框架，利用强化学习（RL）在SAP LE中自主编排仓库任务，提升运营敏捷性和效率。通过将仓库流程建模为动态环境，该框架实现实时优化任务分配、库存移动和订单拣选。基于30万条LE交易的合成数据集，模拟了多语言数据和运营中断等真实场景。分析显示任务优化准确率达95%，相比传统方法处理时间减少60%。可视化工具如效率热力图和性能曲线，辅助制定敏捷仓储策略。该方法解决了数据隐私、可扩展性及SAP集成问题，为现代供应链提供变革性解决方案。
原文摘要 · Abstract (English)
In an era of escalating supply chain demands, SAP Logistics Execution (LE) is pivotal for managing warehouse operations, transportation, and delivery. This research introduces a pioneering framework leveraging reinforcement learning (RL) to autonomously orchestrate warehouse tasks in SAP LE, enhancing operational agility and efficiency. By modeling warehouse processes as dynamic environments, the framework optimizes task allocation, inventory movement, and order picking in real-time. A synthetic dataset of 300,000 LE transactions simulates real-world warehouse scenarios, including multilingual data and operational disruptions. The analysis achieves 95% task optimization accuracy, reducing processing times by 60% compared to traditional methods. Visualizations, including efficiency heatmaps and performance graphs, guide agile warehouse strategies. This approach tackles data privacy, scalability, and SAP integration, offering a transformative solution for modern supply chains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。