A Study on Resource Allocation Efficiency in Multinational Logistics Hubs

Abstract

In the context of globalization, multinational logistics hubs face the paradox of “coexistence of transport capacity redundancy and shortage.” The average equipment idleness rate in North American hubs is 28%, while the container turnover rate gap exceeds 35% during peak seasons. Existing research lacks a three-dimensional collaborative framework of “space – time – transport capacity” for multinational networks, and the models are mostly static. This study constructs a multi-objective mixed-integer programming (MOMIP) model integrating spatial, temporal, and resource constraints. Taking the cross-border multimodal transport network from Los Angeles to Mexico City and Toronto as a case study, the model is verified with millions of operational data from the three major North American hubs from 2022 to 2023. The results show that the model increases transport capacity utilization from 62% to 89%, reduces equipment idleness costs by $4.3 million per year, shortens cross-border transport time by 11.2 hours, and reduces overall costs by 18.5%. The model also demonstrates robustness under fluctuations in oil prices and changes in cargo type structure. This study breaks through the limitations of single-hub research, providing support for corporate cost reduction and efficiency enhancement, as well as for regional logistics policy formulation. It also points out limitations such as the exclusion of air logistics and carbon neutrality goals, and proposes future directions for expanding low-carbon models and supplementing data from Asian hubs.