A commitment of delivery time is critical in some online businesses (De Koster, 2003). An important challenge to meeting customers’ needs is timely order picking which is also relevant to worker safety, item freshness, overall operational synchronization, and reduced overtime. We analyze an order batch picking situation where a trip is constrained by vehicle capacity and must be completed within a specified time. We develop a model which partitions orders to batches to minimize the total travel time such that each trip meets the orders’ time constraints and capacity limit, and also determines a suitable operational policy for each batch. Each policy is characterized by routing method, travel speed, capacity, and pick time. The proposed batching model can simultaneously group orders and can select a best policy among possible policy choices
for each batch. To solve the proposed batching procedure, an exact algorithm is implemented based on a branch-and-price method. Our multiple-policy approach experiences 2.1~7.0% reductions in retrieval time compared to a best single-policy approach. The experimental results emphasize that when time constraints are enforced in order batching, a multiple-policy is preferable to a single-policy approach, because allows additional flexibility.
Progress in Material Handling Research: 2014
Hong, Soondo; Johnson, Andrew L.; and Peters, Brett A., "Order Batching With Time Constraints in a Parallel-aisle Warehouse: a Multiple-policy Approach" (2014). 13th IMHRC Proceedings (Cincinnati, Ohio. USA – 2014). 8.