  The Robotic Mobile Fulfillment Systems (RMFS) is a new type of robotized,
parts-to-picker material handling system, designed especially for e-commerce
warehouses. Robots bring movable shelves, called pods, to workstations where
inventory is put on or removed from the pods. This paper simulates both the
pick and replenishment process and studies the order assignment, pod selection
and pod storage assignment problems by evaluating multiple decision rules per
problem. The discrete event simulation uses realistic robot movements and keeps
track of every unit of inventory on every pod. We analyze seven performance
measures, e.g. throughput capacity and order due time, and find that the unit
throughput is strongly correlated with the other performance measures. We vary
the number of robots, the number of pick stations, the number of SKUs (stock
keeping units), the order size and whether returns need processing or not. The
decision rules for pick order assignment have a strong impact on the unit
throughput rate. This is not the case for replenishment order assignment, pod
selection and pod storage. Furthermore, for warehouses with a large number of
SKUs, more robots are needed for a high unit throughput rate, even if the
number of pods and the dimensions of the storage area remain the same. Lastly,
processing return orders only affects the unit throughput rate for warehouse
with a large number of SKUs and large pick orders.
