基于遗传粒子群算法的低碳多式联运路径优化

Low-Carbon Multimodal Transport Path Optimization Based on Genetic Particle Swarm Algorithm

摘要: 多式联运作为一种高效的物流方式，能够有效整合各种运输方式的优势，降低能源消耗和碳排放。本文针对低碳多式联运路径优化问题，构建出将遗传算法与粒子群算法相融合的混合优化方法，并建立起涵盖运输时间、成本及碳排放要素的多目标优化模型，并设计了一种PSO-GA混合算法，实现了对多式联运路径的综合优化，最后通过算例分析验证了所提方法的有效性和可行性。研究结果表明，PSO-GA混合算法在保证运输效率的同时，能够有效降低多式联运的运输成本以及碳排放，为我国交通运输业的低碳发展提供理论支持和实践指导。

Abstract: As an efficient logistics mode, multimodal transport can effectively integrate the advantages of various transportation modes and reduce energy consumption and carbon emissions. Aiming at the low-carbon multimodal transport path optimization problem, this paper provides a hybrid optimization method combining genetic algorithm and particle swarm optimization algorithm, establishes a multi-objective optimization model considering transport time, cost and carbon emission, and designs a PSO-GA hybrid algorithm to realize the comprehensive optimization of multimodal transport path. Finally, a numerical example is given to verify the effectiveness and feasibility of the proposed method. The results show that the PSO-GA hybrid algorithm can effectively reduce the transportation cost and carbon emissions of multimodal transportation while ensuring transportation efficiency, and provide theoretical support and practical guidance for the low-carbon development of China’s transportation industry.