Optimizing product allocation in a polling-based milkrun picking system
E-commerce fulfillment competition evolves around cheap, speedy, and time-definite delivery. Milkrun order picking systems have proven to be very successful in providing handling speed for a large, but highly variable, number of orders. In this system, an order picker picks orders that arrive in real-time during the picking process; by dynamically changing the stops on the picker’s current picking route. The advantage of milkrun picking is that it reduces order picking set-up time and worker travel time compared with conventional batch picking systems. This article is the first to study order throughput times of multi-line orders in a milkrun picking system. We model this system as a cyclic polling system with simultaneous batch arrivals, and determine the mean order throughput time for three picking strategies: exhaustive, locally-gated, and globally-gated. These results allow us to study the effect of different product allocations in an optimization framework. We show that the picking strategy that achieves the shortest order throughput times depends on the ratio between pick times and travel times. In addition, for a real-world application, we show that milkrun order picking significantly reduces the order throughput time compared with conventional batch picking.
|Keywords||Warehousing, facility logistics, queueing theory, stochastic models, optimization|
|Persistent URL||dx.doi.org/10.1080/24725854.2018.1493758, hdl.handle.net/1765/114270|
van der Gaast, J.P, de Koster, M.B.M, & Adan, I.J.B.F. (2018). Optimizing product allocation in a polling-based milkrun picking system. IIE Transactions, Accepted. doi:10.1080/24725854.2018.1493758