Material handling improvement in warehouses by parts clustering

Abstract

A major part of warehouse operations is related to the collection of parts from the warehouse which is called the Order Picking Problem. To improve order picking operations, the total travel distance and generally picking time must be reduced. In this paper, a two-level approach is proposed that determines the locations of parts in the warehouse. The first step clusters parts into part families. Four different clustering methods based on principal component analysis, singular value decomposition and Two-Step Cluster Component are applied. In the second step, four different heuristics are proposed to determine the locations of parts. In addition to the minimisation of travel distance, we also consider the minimisation of the total congestion in aisles due to multiple workers. The proposed algorithms also consider the interactions between part families to minimise intergroup movements. As a result of the implementation, we achieved more than 40% reduction in material handling compared to the current set-up of the warehouse. The applied algorithms can easily be modified to be used for warehouses with different configurations. The algorithms utilised in this case study can be helpful to researchers to become familiar with new heuristics, as well as practitioners to design improved warehouses.

Keywords:

• order picking problem
• Singular Value Decomposition
• Principal Component Analysis
• Two-Step Cluster Component
• warehouse design
• heuristic

Acknowledgement

We would like to thank the anonymous reviewers for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Indiana Next Generation Manufacturing Competitiveness Center as a part of Technical Assistant Program.