Improving Warehouse Slotting Using Clustering and Genetic Algorithm
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Supply chain management (MPSCM), MSc
Supply chain management (MPSCM), MSc
Publicerad
2024
Författare
Adamah, Nicole
Linnér, Filip
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The optimization of warehouse operations, particularly order picking, is crucial for
reducing 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 picking
efficiency and ergonomics. Using K-Medoids clustering, products were grouped into
clusters, which were then strategically placed in the warehouse through a genetic algorithm to minimize picking distance and improve ergonomic conditions. The study
further refined slotting by optimizing the placement of products within clusters.
The results demonstrate that this AI-driven approach outperforms random slotting
schemes, 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 the
importance of the clustering function’s logic in achieving optimal warehouse slotting
and suggests that a well-designed AI-powered slotting system can lead to substantial operational improvements. A quantitative case study method was employed to
test the algorithm, confirming its effectiveness in a real-world setting. This research
contributes to the field of warehouse management by demonstrating the effective
integration of AI in slotting optimization. The findings provide valuable insights
that can be applied across various industries, paving the way for more intelligent
and effective supply chain solutions.
Beskrivning
Ämne/nyckelord
artificial intelligence , warehouse , supply chain , clustering , slotting , genetic algorithm