Cooccurrence-Based Storage Location Assignment Sub-optimization with Constrained Clustering

Abstract

This study presents an sub-optimization method for storage location assignment aimed at enhancing throughput in automated warehouses characterized by multiple carriers operating on a two-dimensional plane. Our study specifically targets automated warehouses with multiple aisles extending orthogonally from a single common aisle. This type of automated warehouses considers multiple items as a shipping unit, gathering them at designated exits. When items within the same unit are dispersed, carriers often utilize the common aisle, resulting in increased wait times that reduce overall throughput. Therefore, our study introduces an sub-optimization method grounded in item co-occurrence. Through clustering according to their co-occurrence and placing them in close proximity, the frequency of common aisle usage decreases. Consequently, this enhancement positively impacts warehouse throughput. Experimental results validate the superiority of our proposed method over conventional approaches.