Order Picking Optimization in a Distribution Center

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School of Business | Master's thesis
Degree programme
Information and Service Management (ISM)
This study focuses on the order picking process of a distribution center (DC) supplying locomotive and railroad car parts. The DC employs a manual picker-to-parts system where pickers move on foot or by vehicular means. Efficiency of an order picking process in such a DC mainly depends on three problems when the layout of the DC is given: the storage location assignment problem (SLAP), the order batching problem and the order picker routing problem. This study focuses on the aggregate effect three major decisions have on order picking performance in a manual picker-to-parts warehouse. To study the effects of these three sub-problems, we create a framework that allows us to run simulated scenarios with different approaches to these problems. To solve the SLAP, we employ a hybrid of class-based and family grouping methods by enhancing a class-based SKU location assignment with modern clustering techniques. To further improve the order picking process, we experiment with various order batching methods. We use picker routing heuristics to evaluate combinations of the storage location assignments and batching procedures. Over a set of order lines to be fulfilled, the objective function is be the aggregate distance covered over the warehouse floor. We show that distance savings of more than 55% can be achieved by rearranging the DC. Moreover, we show that stock keeping unit (SKU) clustering can improve the performance of class-based storage location assignments and that even simple order batching algorithms are likely to improve order picking performance significantly. Based on the framework, we develop a tool set that encompasses the aspects concerning the DC’s order picking process. The solution will be implemented into a cloud-computing environment, allowing for real-time tracking of the DC’s order picking efficiency and the generation of visual tools that help move SKUs to desirable shelf locations and batch orders.
Thesis advisor
Seppälä, Tomi
order picking, heuristics, clustering, picker routing, distribution center
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