Item Storage Assignment Method Optimization based on Clustering Algorithm
Xinru Wu · 2023
Rising e-commerce orders have intensified warehouse picking workload. The emergence of automated 'goods-to-person' robotic mobile fulfillment system (RMFS) has significantly reduced manual labor in picking. Researching how to assign storage locations for items is a critical issue. Hence, this paper investigates the item storage assignment problem based on mining customer order associations, to minimize the robot workload. This article constructs an item storage assignment model with the objective of minimizing the sum of correlations between items in different groups and improve the spectral clustering algorithm, named the Kmeans-based spectral clustering algorithm(KSCA), to solve the model. Computational experiments are conducted to verify the effectiveness and efficiency of the improved algorithm and gain insights into the daily warehouse operations.