Improving Order Picking Efficiency by Analyzing Combinations of Storage, Batching, Zoning, and Routing Policies

Abstract

In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. The objective of this research is to simulate and evaluate the interaction between several storage, batching, zone picking and routing policies in order to reduce the order picker travel distance. The value of integrating these four operation policy decisions is proven by a real-life case study. A full factorial ANOVA provides insight into the interactions between storage, batching, zoning, and routing policies. The results of the study clearly indicate that warehouses can achieve significant benefits by considering storage, batching, zone picking, and routing policies simultaneously. Awareness of the influence of an individual policy decision on the overall warehouse performance is required to manage warehouse operations, resulting in enhanced customer service.

Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse

“…In accordance with previous research, the batch capacity is expressed in number of order lines (Valle et al, 2017), assuming that sorting activities should be performed afterwards. An order can be only assigned to a single batch (i.e., order integrity) (Van Gils et al, 2016). Each batch is assigned to a batch position of an order picker in order to define the sequence in which a picker should pick the batches assigned to him/her.…”

“…The complexity of planning order picking operations results from the interdependencies among the wide range of planning problems (e.g., storage assignment, order batching, routing, picker scheduling). Warehouses can achieve significant efficiency benefits by considering these interdependencies (Van Gils et al, 2016). Serving e-commerce markets, globalisation and increased customer expectations further increase the complexity of managing order picking operations.…”

Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions

“…In order to analyze the effect of combining storage, batching, zoning, and routing, real-life data are used. We use the case study of Van Gils et al (2016) that is based on a large warehouse located in Belgium to show the practical relevance of integrating storage, batching, zone picking, and routing policy decisions. The warehouse stores approximately 90,000…”

Reducing Picker Blocking in a High-Level Narrow-Aisle Order Picking System

“…A recent literature review of Van Gils et al (018b) shows that few articles combine multiple planning problems, despite the strong relation among order picking planning problems. Additionally, previous research on order picking systems is subject to a large number of assumptions to simplify operations, such as ignoring picker blocking, considering low-level storage locations, and disregarding precedence constraints and safety constraints (Mellema and Smith 1988;Petersen and Aase 2004;Shqair et al 2014;Van Gils et al 2016).…”