This study proposes an innovative scientific framework for optimizing the placement of cross-docking facility projects within international logistics networks. The framework integrates an extended K-mean optimization and GIS based framework into a multi-project management to enable more accurate and spatially informed decision-making. A comprehensive review of the literature indicates that road freight transportation and inventory holding costs represent the largest components of total logistics expenditure. In international road freight operations, direct delivery is frequently impractical, thereby necessitating the use of intermediate transshipment hubs, such as cross-docking facilities. However, inefficient selection of these intermediary nodes may increase inventory storage requirements and transportation costs. Consequently, the accurate identification of optimal cross-docking locations has substantial potential to reduce transport distances and associated operational expenses across global logistics networks. To examine this proposition, two comparative scenarios were developed. The first scenario represents the Traditional Cross-Docking project (TCD) approach, in which cross-docking activities are conducted at national border points before international distribution. The second scenario applies the proposed K-mean optimization and GIS based framework into a multi-project management (KGMP) to identify optimal cross-docking locations beyond border regions across the wider international supply chain network. Both scenarios were assessed through numerical simulations and analytical evaluation to compare their effectiveness in minimizing transportation distances. Following this, the simulation results demonstrate that the proposed KGMP framework significantly reduces international transport routes compared with the conventional border-based configuration. These findings highlight the strategic importance of facility location decisions in international logistics planning, where optimized cross-docking placement can enhance transportation efficiency, operational cost reduction, and a greater administrative competitiveness in increasingly complex global markets.
Bootdachi et al. (Thu,) studied this question.