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Demand-driven storage allocation for optimizing order picking processes
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AI
DOI：10.1016/j.eswa.2025.126812.png)
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Abstract
En 中文
The rapid growth of e-commerce has significantly increased the demand for logistics services that facilitate the efulfillment process. This process involves several key activities including order reception, storage, picking, packing, and delivery. Among these activities, order picking is particularly critical because it is time-consuming and labor-intensive. Numerous studies have focused on improving the order-picking efficiency. However, warehouse operations are highly interconnected, and ineffective storage allocation can adversely impact the order-picking performance. Notwithstanding this interrelationship, limited research has evaluated the impact of storage allocation and order picking. This study proposes an intelligent forecasting for order picking optimization (IFOPO) model. It is designed to enhance the order picking efficiency by optimizing the space allocation in both pick face and bulk storage areas through demand forecasting. Utilizing machine learning technology, the model predicts the item demand to strategically allocate storage space, thereby reducing the travel distances for order pickers. The proposed IFOPO model aims to enhance the overall e-fulfillment efficiency by emphasizing the relationship between the critical components of storage allocation and order picking. This research provides effective insights into the interplay between demand forecasting, storage allocation, and order picking. Thus, it contributes to a holistic approach to warehouse optimization strategies.
Keywords:
E-commerce
Warehouse Management
Logistic Industries
Artificial Intelligence
Demand Forecasting
Pick Face
Order Picking
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W