A polling-based dynamic order picking system for online retailers

Abstract

One of the challenging questions that online retailers are currently facing is how to organize the logistic fulfillment processes both during and after a transaction has taken place. As new information technologies become available that allow picking information to be conveyed in real time and with the ongoing need to create greater responsiveness to customers, there is increasing interest in applying dynamic picking in the warehouses of online retailers. In a Dynamic Picking System (DPS), a worker picks orders that arrive in real time during the picking operations and the picking information can dynamically change in a picking cycle. Models to describe and analyze such systems via stochastic polling theory are presented and closed-form expressions for the order line waiting times in a DPS are derived. These analytical results are verified by simulation. It is shown that the application of polling-based picking can generally lead to shorter order throughput times and higher on-time service completion ratios than traditional batch picking systems using optimal batch sizes. It is demonstrated that the proposed analysis method can be applied to minimize warehouse cost and improve service.