Optimizing product allocation in a polling-based milkrun picking system

Abstract

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 setup 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.

The forgotten sons: Warehousing systems for brick-and-mortar retail chains

“…In these systems, pickers repeated follow a fixed path. At each location passed, the pickers are displayed the total number of pieces demanded by the orders that have arrived since their previous visit ( Van der Gaast, De Koster, & Adan, 2019;Gong & De Koster, 2008 ). However, these systems require an additional consolidation stage to sort the orders.…”

“…Scientific decision support when to apply multiple warehousing systems and which combinations fit what branches of industry are yet missing. ( Bischoff, 2006;Crainic et al, 2009;Faroe et al, 2003;Junqueira et al, 2012;Kang et al, 2012;Lodi et al, 2002b;Martello et al, 2000 ) Pick-and-pass systems for large orders ( Armbruster et al, 2007;Bartholdi & Eisenstein, 1996;Bartholdi et al, 2001;Jane, 2000;Jane & Laih, 2005;Jewkes et al, 2004;Koo, 2009;Melacini et al, 2011;Van der Gaast et al, 2018;Otto et al, 2017;Yu & De Koster, 2008 ) ( Füßler et al, 2019b;Jane, 2000;Jane & Laih, 2005;Jewkes et al, 2004;Van der Gaast et al, 2019;Otto et al, 2017;Pan et al, 2015;Pan & Wu, 2009;Yu & De Koster, 2008 ) ( Füßler et al, 2019a ) Crane-supplied pick face ( Ramtin & Pazour, 2014 ) ( Ramtin & Pazour, 2015 ) ( Boysen & Stephan, 2016;Kim et al, 2003;Schwerdfeger & Boysen, 2017;Yu & De Koster, 2010 ) Bulk picking and store consolidation ( Bozer & Sharp, 1989;Johnson & Lofgren, 1994;…”

Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework

“…Guo et al [289] analyzed through simulations the local return strategy and conclude that this strategy can improve system performance by reducing the average throughput time, although it is influenced both by the storage strategy and the size of the return area. A similar strategy is proposed by van der Gaast et al [290], with a "Milk run order picking system"; in this system, pickers can pick orders in real-time during the picking process, as soon as they arrive. In their simulation model they compared the batch picking strategy showing that it can reduce the order throughput time significantly.…”

Practical factors in order picking planning: state-of-the-art classification and review

“…Finally, an interventionist policy even allows newly arriving orders to be added to batches that are picked at the moment of solving the batching, routing and job assignment problems. In this last policy, the real-time order arrival can result in very small order throughput times (Giannikas et al 2017;Van der Gaast, De Koster, and Adan 2019;Gong and De Koster 2008).…”