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<p>Against the backdrop of global low-carbon transformation and improved logistics efficiency, the optimization of multimodal transport routes for container transport in ports has become a key issue. This study introduces a multi-objective multimodal transport route optimization model and employs a two-stage hybrid algorithm based on Dijkstra’s algorithm and an improved ant colony algorithm for solution. The method utilizes Dijkstra’s algorithm to generate initial feasible paths to narrow the search space, and then enhances the global search and convergence performance of the ant colony algorithm by introducing mechanisms such as dynamic pheromone evaporation, adaptive heuristic functions, and elite strategies. The current model assumes a deterministic environment with fixed transportation costs, times, and emission factors, without accounting for real-time stochastic disturbances such as port congestion, rail delays, or weather conditions. Experimental findings demonstrate that the introduced algorithm achieves a Pareto front coverage of 0.92 after 80 iterations, with an optimal comprehensive objective value of 0.467. Compared with the standard Dijkstra’s algorithm-based shortest path solution, the proposed method achieves a carbon emission reduction rate of 18.73% for the selected Pareto-optimal solution that balances cost, time, and emissions, while increasing cost and time by only 5.17% and 3.81%, respectively. These percentage increases are global values specific to this particular Pareto-optimal solution, not averages across the entire Pareto front. In road, rail, and waterway transport, the carbon emissions of the routes planned by the algorithm are 142.35&#xa0;kg, 98.76&#xa0;kg, and 76.32&#xa0;kg, respectively, with corresponding declines in cost and temporal requirements. The research outcomes show that the introduced low-carbon port container multimodal transport route optimization method can effectively coordinate economic, timeliness and environmental protection goals, reduce carbon emissions while safeguarding the efficiency of transportation operations, and provide feasible decision support for low-carbon port container transport.</p>

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Low-Carbon Port Container Multimodal Transport Route Optimization Based on Dijkstra’s Algorithm and Improved Ant Colony Algorithm

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Xueqing Wang

摘要

Against the backdrop of global low-carbon transformation and improved logistics efficiency, the optimization of multimodal transport routes for container transport in ports has become a key issue. This study introduces a multi-objective multimodal transport route optimization model and employs a two-stage hybrid algorithm based on Dijkstra’s algorithm and an improved ant colony algorithm for solution. The method utilizes Dijkstra’s algorithm to generate initial feasible paths to narrow the search space, and then enhances the global search and convergence performance of the ant colony algorithm by introducing mechanisms such as dynamic pheromone evaporation, adaptive heuristic functions, and elite strategies. The current model assumes a deterministic environment with fixed transportation costs, times, and emission factors, without accounting for real-time stochastic disturbances such as port congestion, rail delays, or weather conditions. Experimental findings demonstrate that the introduced algorithm achieves a Pareto front coverage of 0.92 after 80 iterations, with an optimal comprehensive objective value of 0.467. Compared with the standard Dijkstra’s algorithm-based shortest path solution, the proposed method achieves a carbon emission reduction rate of 18.73% for the selected Pareto-optimal solution that balances cost, time, and emissions, while increasing cost and time by only 5.17% and 3.81%, respectively. These percentage increases are global values specific to this particular Pareto-optimal solution, not averages across the entire Pareto front. In road, rail, and waterway transport, the carbon emissions of the routes planned by the algorithm are 142.35 kg, 98.76 kg, and 76.32 kg, respectively, with corresponding declines in cost and temporal requirements. The research outcomes show that the introduced low-carbon port container multimodal transport route optimization method can effectively coordinate economic, timeliness and environmental protection goals, reduce carbon emissions while safeguarding the efficiency of transportation operations, and provide feasible decision support for low-carbon port container transport.