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The study employed a simulation based on a detailed dataset containing over 307,046 unique order identifiers and 1,050 unique product identifiers. This dataset included information such as order placement dates, product codes, quantities, and precise locations within the warehouse, including coordinates. The simulation modeled the order-picking route using the Single-Picker Routing Problem (SPRP) algorithms to minimize distance and travel time. The methods compared various wave-picking strategies and grouping methods (single-line and multi-line) for their effectiveness.The applied method significantly reduced the travel distance required by the order picker in the warehouse. The key to this optimization was consolidating orders into waves of specific sizes, achieving a fourfold distance reduction for the studied dataset. Additionally, the solution proposed grouping products by location within the warehouse, either in a single aisle or across multiple aisles based on proximity. Although this method often enhances efficiency, it did not in this particular case. However, it was included as it may yield better results with different datasets and further reduce travel distances in the warehouse. The research underscored the critical role of efficient routing and grouping strategies in warehouse operations. Although wave picking significantly reduced travel distances, the effectiveness of clustering strategies depended on the characteristics of the specific dataset, suggesting the need for tailored solutions based on the warehouse layout and the features of the orders. Future research could extend to integrating product volume and weight variations, which may further optimize order-picking strategies.
Rymarczyk et al. (Tue,) studied this question.