Order picking is considered one of the most time consuming and costly processes in warehouse operations. Tardiness in an order picking process not only causes low throughput and reduced efficiency, but delays the entire supply chain marketing response. This motivates researchers to make this labor-intensive activity more efficient. Many order picking systems, such as wave picking, pick-pass system etc., have been designed while integrating warehouse layout, retrieval strategies, and routing methods. However, it is difficult to implement the redesigned system while operating. An approach with fewer changes to storage assignments and process reconstructions is preferred. This paper focuses on designing an efficient picking schedule with the consideration of equipment selection and shortest routes. The retrieval distance is minimized through re-batching stock keeping units (SKU) and routing optimization. Compared to the traditional pick-by-order strategy, the order is decomposed and re-batched at the SKU level. A mixed integer programming model is proposed to determine how SKUs from different orders are batched and how the routing and equipment are scheduled. Experimental results on a large order dataset show the capability in reducing traveling distance.
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