Order batching in a pick-and-pass warehousing system with group genetic algorithm

• Order batching method is developed based on group genetic algorithm for a pick-and-pass system.
• Balance of workloads among all pickers has a great impact on efficiency of pick-and-pass system.
• Proposed heuristic policy improves the order picking efficiency in a pick-and-pass system.

An order batching policy determines how orders are combined to form batches. Previous studies on order batching policy focused primarily on classic manual warehouses, and its effect on pick-and-pass systems has rarely been discussed. Pick-and-pass systems, a commonly used warehousing installation for small to medium-sized items, play a key role in managing a supply chain efficiently because the fast delivery of small and frequent inventory orders has become a crucial trading practice because of the rise of e-commerce and e-business. This paper proposes an order batching approach based on a group genetic algorithm to balance the workload of each picking zone and minimize the number of batches in a pick-and-pass system in an effort to improve system performance. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm, and compare the throughput for different order batching policies. The results reveal that the proposed heuristic policy outperforms existing order batching policies in a pick-and-pass system.