Dynamic Picking and Storage Optimization of Robotic Picking Systems

With the development of e-commerce and the improvement of logistics requirements, more and more ‘parts-to-picker’ picking systems begin to replace the inefficient ‘picker-to-parts’ picking systems in various scenarios. As the mainstream ‘parts-to-picker’ system, the robotic mobile fulfillment system has been attracting much attention in recent years. In addition to the customer's changing requirements, the rapid response of the picking system to the order is particularly important. In the above context, to seek a breakthrough in the picking system's picking efficiency without increasing the cost of additional equipment, the storage allocation of the pods becomes very important. This article focuses on the dynamic storage allocation of robotic mobile fulfillment system, which has positive theoretical and practical significance. By analyzing the pod storage process of the robotic mobile fulfillment system, a dynamic pod storage allocation model suitable for the robotic mobile fulfillment system is established with the goal of minimizing the pod handling distance. Two dynamic pod storage allocation strategies are proposed for the system. By simulating the picking systems of different scales, the effectiveness of the dynamic storage allocation strategy is verified, which has a certain reference to the operation of the robotic mobile fulfillment system in practice.

Assessment of working postures and physical loading in advanced order picking tasks: A case study of human interaction with automated warehouse goods-to-picker systems

BACKGROUND: Order picking activities are the most labor-intensive processes in retail warehouses. Although various automated order picking technologies have been developed recently, human intervention continues to be required. Most advanced order picking systems can be classified into automated storage and retrieval system (AS/RS) and moving robot types. Both types of goods-to-picker systems aim to reduce the picker’s travel time required to determine product locations and to move these products to meet customer requests. OBJECTIVE: Many studies on the efficiency and effectiveness of automated order picking systems have focused solely on system performance. Since human operators play an essential part in order picking systems from both the effectiveness and efficiency point of view, the work-related risk factors for the workers interacting with these systems should also be evaluated. In this paper, we assess the ergonomic design features of two system types, a moving robot (MR) and automated storage and retrieval system (AS/RS), focusing on the assessment of the risk factors for work-related postural stresses. METHODS: We compare the performance factors of two order picking systems, i.e. MR and AS/RS, by applying a digital human modeling and simulation, and assessing the total average physical activity exhibited by human operators on a given order picking task. RESULTS: The AS/RS type order picking system exhibited a lower risk for task-related postural stresses for warehouse workers. CONCLUSIONS: The picking station for moving robot (MR) order picking system requires design changes in order to reduce postural stresses during human operator’s interaction with such a system.