Aggregate modeling for flow time prediction of an end-of-aisle order picking workstation with overtaking

An aggregate modeling methodology is proposed to predict flow time distributions of an end-of-aisle order picking workstation in parts-to-picker automated warehouses with overtaking. The proposed aggregate model uses as input an aggregated process time referred to as the effective process time in combination with overtaking distributions and decision probabilities, which we measure directly from product arrival and departure data. Experimental results show that the predicted flow time distributions are accurate, with prediction errors of the flow time mean and squared coefficient of variation less than 4% and 9%, respectively. As a case study, we use data collected from a real, operating warehouse and show that the predicted flow time distributions resemble the flow time distributions measured from the data.