This paper explores the integration of Automated Guided Vehicles (AGVs) in warehouse order picking, a crucial and cost-intensive aspect of warehouse operations. The booming AGV industry, accelerated by the COVID-19 pandemic, is witnessing widespread adoption due to its efficiency, reliability, and cost-effectiveness in automating warehouse tasks. This paper focuses on enhancing the picker-to-parts system, prevalent in small to medium-sized warehouses, through the strategic use of AGVs. We discuss the benefits and applications of AGVs in various warehouse tasks, highlighting their transformative potential in improving operational efficiency. We examine the deployment of AGVs by leading companies in the industry, showcasing their varied functionalities in warehouse management. Addressing the gap in research on optimizing operational performance in hybrid environments where humans and AGVs coexist, our study delves into a dynamic picker-to-parts warehouse scenario. We propose a novel approach Neural Approximate Dynamic Programming approach for coordinating a mixed team of human and AGV workers, aiming to maximize order throughput and operational efficiency. This involves innovative solutions for non-myopic decision making, order batching, and battery management. We also discuss the integration of advanced robotics technology in automating the complete order-picking process. Through a comprehensive numerical study, our work offers valuable insights for managing a heterogeneous workforce in a hybrid warehouse setting, contributing significantly to the field of warehouse automation and logistics.

翻译：本文研究了自动导引车（AGV）在仓库订单拣选中的集成问题——这是仓库运营中一项关键且成本密集的环节。受新冠疫情加速发展的AGV产业正因其在自动化仓库任务中展现的高效性、可靠性和成本效益而获得广泛采用。本文聚焦于通过战略性地运用AGV来优化中小型仓库中普遍采用的"人至货"系统。我们探讨了AGV在各类仓库任务中的优势与应用，重点阐述其在提升运营效率方面的变革潜力。通过分析行业领先企业的AGV部署案例，展示了其在仓库管理中的多样化功能。针对人机共存混合环境中运营优化研究存在的空白，本研究深入探讨了动态"人至货"仓库场景。我们提出了一种新颖的神经近似动态规划方法，用于协调人类与AGV工人的混合团队，旨在最大化订单吞吐量和运营效率。该方法涵盖非短视决策、订单分批及电池管理的创新解决方案。同时讨论了先进机器人技术在实现完整订单拣选流程自动化中的整合应用。通过全面的数值研究，本文为管理混合仓库环境中的异质劳动力提供了宝贵见解，对仓库自动化与物流领域做出了重要贡献。