Abstract

To address the multi-objective conflicts among cost, time, and carbon emissions in Guangxi cross-border container multimodal transport—under the influence of factors such as tariffs, fluctuating customs clearance efficiency, and carbon emission policies—this paper develops a multi-objective optimization model that integrates mixed time windows and scenario analysis. The model incorporates multiple elements, including transportation cost, transshipment cost, tariff cost, time value of cargo, carbon tax cost, in-transit and customs clearance time, and transshipment-related carbon emissions, making it more aligned with real-world cross-border operational scenarios. To effectively solve this complex model, an improved NSGA-II algorithm (I-NSGA2) is designed, which introduces an adaptive crossover and mutation operator along with an elite retention strategy to enhance convergence speed and solution diversity, while embedding a scenario parameter response mechanism to accommodate dynamic fluctuations in key parameters. Subsequently, an evaluation framework is constructed using the entropy weight–TOPSIS method to select recommended routes with favorable cost–time–carbon trade-offs from the Pareto frontier. A case study based on the Nanning–Kuala Lumpur route is conducted for validation. Experimental results demonstrate that the I-NSGA2 algorithm significantly outperforms MOPSO, MOEAD, and the standard NSGA-II in terms of IGD and HV metrics; time-sensitive cargo tends to favor rail-dominated routes, while low-cost cargo prefers combined road–water–rail routes. This study effectively addresses the route selection problem for Guangxi cross-border container multimodal transport under varying key parameters, and also provides a research foundation for optimizing cross-border multimodal transport route selection in other regions.

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