A simulation study on the robotic mobile fulfillment system in high-density storage warehouses

Abstract The robotic mobile fulfillment system (RMFS) is a parts-to-picker material handling system that has emerged in e-commerce warehouses and aims to save labor costs and achieve higher picking efficiency. This study investigates an RMFS in high-density storage warehouses with limited space or high rental costs. Supposing that the incoming orders have been preprocessed, we focus on how to carry specific storage pods to given workstations and then return to the storage area, which are called tasks. We elaborate on the task fulfillment process, which includes three modules: task assignment, path planning, and traffic control. We characterize the unique nature of high-density warehouse systems by introducing some definitions, rules, and propositions. To evaluate the system performance, a simulation platform is implemented to compare the traditional and high-density storage warehouse layouts with different robot numbers. The results show that the RMFS with a high-density storage layout can save approximately 10% storage space on average while maintaining the same level of robot deployment and energy consumption. The results also show that we can achieve a higher warehouse space utilization while maintaining comparable efficiency by adopting more robots. We also observe that a large lane depth, for example, with four unit spaces, is not appealing due to high throughput time and energy consumption.