Purpose This study investigates the performance of Autonomous Mobile Robot (AMR)–human collaboration in hybrid warehouse order-picking operations. It aims to identify optimal resource allocation strategies and assess the role of warehouse layout design, particularly cross-aisles, in enhancing operational efficiency in high-velocity retail and e-commerce environments. Design/methodology/approach We developed a discrete-event simulation model to explore 48 warehouse configurations varying AMR and human picker counts across traditional and cross-aisle layouts. We employed polynomial regression, factorial interaction models, response surface methodology (RSM), and post hoc cost-efficiency simulations (1,200 scenarios) to capture operational and economic performance under real-world constraints. Findings Results demonstrate that warehouse throughput and efficiency are maximised at an AMR-to-picker ratio of approximately 2:1. While cross-aisle layouts offered modest gains in navigation flexibility, but their impact on throughput was not statistically significant. However, the cost-efficiency analysis consistently identified a small set of resource combinations that delivered more than two pallets per dollar, confirming the robustness of optimal ratios across wage and cost variations. The study also uncovered diminishing returns at higher automation levels, reinforcing the need for balanced resource planning. Practical implications This research provides warehouse and operations managers with a data-driven framework for assessing AMR investments and workforce allocation. The study aids strategic decision-making regarding hybrid system design, layout planning, and automation scaling within budget constraints by correlating performance with cost metrics. Originality/value This study addresses a significant gap in the warehouse logistics literature by empirically investigating AMR-human collaboration through dynamic simulations. It combines layout design, real-time task updates, and financial feasibility into a single comprehensive framework. It builds upon previous static optimisation models and offers new insights into hybrid automation systems within practical distribution settings.
Garg et al. (Mon,) studied this question.