Robotic order picking in cellular warehousing based on a time–space–state network

Goods-to-person order picking, such as robotic mobile fulfillment systems, is prevalent in e-commerce order fulfillment. However, the efficiency of robot transportation is hindered by the need to transport entire pods containing numerous stock-keeping units (SKUs), most of which are not required for the orders being fulfilled. This paper considers a new robotic cellular warehousing system that achieves efficient robot transportation by having robots transport only the SKUs requested in an order and by enabling them to perform both transport and picking operations. This is accomplished through a multi-robot parallel picking scheme, which involves three key stages of planning: (i) construction of order routes, (ii) multi-robot order allocation, and (iii) multi-robot conflict-free path planning. To facilitate the planning problems, we propose a dedicated time–space–state network model that considers three-dimensional information to identify feasible and efficient paths for robotic order picking. Our proposed approach is validated through computational experiments on realistic instances, and we also identify the optimal system configuration, including length-to-width ratio, robot fleet size, and their impacts on picking efficiency. The computational results and analyses offer practical managerial implications for warehouse operators, providing insights for decision-making.