A K-Means Clustering and the Prim’s Minimum Spanning Tree-Based Optimal Picking-List Consolidation and Assignment Methodology for Achieving the Sustainable Warehouse Operations

Abstract

Rapid industrialization has caused the concentration of greenhouse gases in the atmosphere to increase rapidly, leading to drastic global climate changes and ecological degradation. To establish a sustainable supply chain for consumer electronic products, this study focuses on warehouse operations and develops a K-means clustering and Prim’s minimum spanning tree-based optimal picking-list consolidation and assignment methodology. Compact camera modules are used to demonstrate and verify the effectiveness of this methodology. This methodology can be divided into two parts. First, the K-means clustering method is applied to conduct a picking-list consolidation analysis to create an optimal picking-list consolidation strategy for sustainable warehouse operations. Second, the most similar picking lists in each cluster are connected using Prim’s minimum spanning tree algorithm to generate the connected graph with the minimum spanning tree so as to establish a picking-list assignment strategy for sustainable warehouse operations. In this case study, this to-be model substantially reduced the traveling distance of the electric order-picking trucks within a warehouse and increased the picking efficiency to diminish the carbon emissions toward a sustainable supply chain.