Author
Listed:
- Sebastian Henn
(Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)
- Sören Koch
(Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)
- Gerhard Wäscher
(Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)
Abstract
Order picking is a warehouse function dealing with the retrieval of articles from their storage location in order to satisfy a given demand specified by customer orders. Of all warehouse operations, order picking is considered to include the most cost-intensive ones. Even though there have been different attempts to automate the picking process, manual order picking systems are still prevalent in practice. This article will focus on order batching, one of the main planning issues in order picking systems. Order Batching has been proven to be pivotal for the efficiency of order picking operations. With respect to the availability of information about the customer orders, order batching can be distinguished into static batching and dynamic batching. Improved order batching reduces the total picking time required to collect the requested articles. According to experience from practice, this can result in significant savings of labor cost and into a reduction of the customer order's delivery lead time. The aim of this contribution is to provide comprehensive insights into order batching by giving a detailed state-of-the-art overview of the different solution approaches which have been suggested in the literature. Corresponding to the available publications, the emphasis will be on static order batching. In addition to this, the paper will also review the existing literature for variants and extensions of static order batching (e.g. due dates, alternative objective functions). Furthermore, solution approaches for dynamic order batching problems (like time window batching) will be presented.
Suggested Citation
Sebastian Henn & Sören Koch & Gerhard Wäscher, 2011.
"Order Batching in Order Picking Warehouses: A Survey of Solution Approaches,"
FEMM Working Papers
110001, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
Handle:
RePEc:mag:wpaper:110001
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Citations
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Cited by:
- De Santis, Roberta & Montanari, Roberto & Vignali, Giuseppe & Bottani, Eleonora, 2018.
"An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses,"
European Journal of Operational Research, Elsevier, vol. 267(1), pages 120-137.
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