Enhancing Warehouse Performance by Efficient Order Picking Defended on Thursday, 23 October 2008

This thesis studies order picking in warehouses. Order picking, the process of retrieval products from their storage locations to fill customer orders, is regarded as the most critical operation in a warehouse. Using stochastic modelling, we develop a model to estimate order picking system performance on various design alternatives and operating policies. The model is fast, flexible, and sufficiently accurate for practical purposes. The thesis introduces a concept of Dynamic Storage. In a Dynamic Storage System (DSS), orders are picked in batches and only those products needed for the current pick batch are retrieved from a reserve area and are stored in the pick area, just in time. Through analytical and simulation models, we demonstrate a DSS can substantially improve order throughput and reduce labour cost simultaneously over conventional order picking systems, where all the products required during a pick shift are stored in the pick area. The thesis also studies an internal distribution process at a flower auction company. Based on simulation and optimization models, we propose ways to reduce congestion and improve order lead time.