Two-stage Storage Assignment to Minimize Travel Time and Congestion for Warehouse Order Picking Operations

Abstract This research presents a systematic and integrated approach that extends the correlated storage assignment strategy to improve the efficiency of warehouse order picking operations. The correlated storage assignment can reduce a significant amount of travel costs, but could lead to traffic congestion due to the imbalanced traffic flow. Hence, this research proposes the correlated and traffic balanced storage assignment (C&TBSA) to minimize the travel time and picking delays, which is modeled in two stages: clustering and assignment. In the clustering stage, a bi-objective optimization model is formulated to group items with the consideration of both travel efficiency and traffic flow balance, which is solved using multi-objective evolutionary algorithms (MOEAs). In the assignment stage, items in each cluster are distributed to the available storage locations. C&TBSA is evaluated with an actual warehouse case study and the results show that C&TBSA outperforms random, class-based, and correlated storage assignment methods by 48.74%, 23.82%, and 7.58% respectively, regarding the total time consisting of travel time and picking delays.