Dynamic Task Allocation in Intelligent Warehouses with Hybrid Workforce of Automated Guided Vehicles and Human Pickers

This article 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. Through the strategic use of AGVs, this article focuses on enhancing the picker-to-parts system, which involves workers travelling to item locations, collecting them, and moving to the next location. We propose a novel MDP model for coordinating a hybrid team of human and AGV workers, aiming to maximize order throughput and operational efficiency, and employ a Neural Approximate Dynamic Programming (NeurADP) approach as the solution method. Specifically, our solution framework involves innovative solutions for non-myopic decision making, order batching, and battery management. The numerical results demonstrate that the NeurADP policy outperforms all benchmark policies, including both myopic and non-myopic ones, with a 3.32% and 5.44% improvement in order fulfillment over the alternatives. Comprehensive empirical analysis offers valuable insights for managing a heterogeneous workforce in a hybrid warehouse setting, highlighting the contributions of our work to the field of warehouse automation and logistics.