Batching orders in warehouses by minimizing travel distance with genetic algorithms

The power of warehousing system to rapidly respond to customer demands participates an important function in the success of supply chain. Before picking the customer orders, effectively consolidating orders into batches can significantly speed the product movement within a warehouse. There is considerable product movement within a warehouse; the warehousing costs can be reduced by even a small percentage of reduction in the picking distance. The order batching problem is recognized to be NP-hard, and it is extremely difficult to obtain optimal solutions for large-scale problems within a tolerable computation time. Previous studies have mainly focused on the order batching problems in warehouses with a single-aisle and two-dimension layout. This study develops an order batching approach based on genetic algorithms (GAs) to deal with order batching problems with any kind of batch structure and any kind of warehouse layout. Unlike to previous batching methods, the proposed approach, additionally, does not require the computation of order/batch proximity and the estimation of travel distance. The proposed GA-based order batching method, namely GABM, directly minimizes the total travel distance. The potential of applying GABM for solving medium- and large-scale order batching problems is also investigated by using several examples. From the batching results, the proposed GABM approach appears to obtain quality solutions in terms of travel distance and facility utilization.