An advanced optimization framework for cross-docking site selection in global supply chains using an enhanced k-means clustering algorithm integrated with geographic information systems

This study introduces a novel mathematical framework for the optimal placement of cross-docking facilities within international logistics networks. The model employs an extended K-means clustering algorithm integrated with Geographic Information Systems (GIS) to enhance spatial decision-making. A comprehensive review of the existing literature highlights that transportation and warehousing costs represent the most substantial components of overall logistics expenditures. In international land transportation, direct point-to-point delivery is often impractical, thereby necessitating intermediate transshipment through cross-docking facilities. Inefficient selection of these intermediary nodes can result in elevated storage and transportation expenses. Accurate identification of optimal cross-docking locations, therefore, has significant potential to reduce both transport distances and associated costs in global logistics operations. To examine this premise, two comparative scenarios were developed. The first assumes that cross-docking operations are conducted at national borders prior to international shipment, while the second applies the proposed extended K-means clustering algorithm integrated with GIS to determine optimal cross-docking points beyond the border within the broader international supply chain network. Both scenarios were subjected to numerical simulations and analytical assessments to evaluate their relative performance in minimizing transportation distances. The results reveal that the GIS-supported extended K-means approach produces substantially shorter international transport routes compared with the border-based cross-docking strategy. These findings emphasize the strategic importance of accurately locating cross-docking facilities in international logistics planning. By optimizing cross-docking placement, logistics managers can enhance transportation efficiency, reduce operational costs, and strengthen organizational competitiveness in the increasingly dynamic global marketplace.