Optimising Warehouse Order Picking: Real Case Application in the Shoe Manufacturing Industry

Abstract

Order picking is a critical and labour-intensive warehouse management operation that involves removing items from storage locations to fulfil customer orders. This paper analyses a new order-picking problem based on the real case of a Canadian shoe manufacturer characterised by a warehouse with random storage, where different product types can be assigned to a single storage location. While maximising space utilisation, considering the high number of Stock Keeping Units, this storage approach makes the creation of efficient picking routes challenging, increasing the effort needed to complete picking orders. To address this challenge, we present the Genetic Route Optimisation algorithm for optimising order-picking routes. Our methodology involved testing the proposed algorithm using real-world data derived from the company’s Warehouse Management System. The results demonstrate a reduction in picking distances, highlighting the effectiveness of the Genetic Route Optimisation algorithm in optimising picking routes in a random storage environment. As well as presenting a practical application case, the study highlights the potential of the proposed algorithm to improve operational efficiency in warehouse environments. It also paves the way for future research in warehouse logistics, especially by adapting similar algorithmic strategies to various complex and dynamic warehouse environments, thus advancing the field of warehouse management.

Keywords

• Random storage location
• mixed shelving
• picker routing
• genetic algorithm