Improving Warehouse Slotting Using Clustering and Genetic Algorithm

Sammanfattning

The optimization of warehouse operations, particularly order picking, is crucial forreducing operating costs and enhancing ergonomics to prevent work-related injuries.This thesis addresses the challenge of optimizing slotting in manually operated warehouses by integrating clustering and genetic algorithms to improve order pickingefficiency and ergonomics. Using K-Medoids clustering, products were grouped intoclusters, which were then strategically placed in the warehouse through a genetic algorithm to minimize picking distance and improve ergonomic conditions. The studyfurther refined slotting by optimizing the placement of products within clusters.The results demonstrate that this AI-driven approach outperforms random slottingschemes, significantly reducing picking distance and enhancing ergonomic safety.Moreover, clustering using AI methods produces more well-defined and evenly distributed clusters compared to traditional ABC analysis. The study highlights theimportance of the clustering function’s logic in achieving optimal warehouse slottingand suggests that a well-designed AI-powered slotting system can lead to substantial operational improvements. A quantitative case study method was employed totest the algorithm, confirming its effectiveness in a real-world setting. This researchcontributes to the field of warehouse management by demonstrating the effectiveintegration of AI in slotting optimization. The findings provide valuable insightsthat can be applied across various industries, paving the way for more intelligentand effective supply chain solutions.