不确定条件下公铁水多式联运多目标路径优化研究

Research on Multi-Objective Path Optimization of Highway-Railway-Waterway Multimodal Transport Under Uncertain Conditions

多式联运运输的时效性和成本在现代物流的发展中是不可忽视的因素。针对公铁水多式联运的运输目标,主要研究了当运输时间、中转时间双重不确定因素服从随机分布时的绿色多式联运路径优化问题。构建以运输时间、碳排放、运输成本为目标函数,碳排放量为约束,建立运输时间、中转时间双重不确定条件下绿色多式联运路径多目标优化模型。并据此采用模糊自适应遗传算法(FAGA)和快速非支配排序遗传算法(NSGA-Ⅱ)设计多式联运路径优化策略;最后采用从南昌到柏林的路径数据仿真验证所提方法的有效性并对结果进行对比分析。研究发现基于NSGA-Ⅱ算法的多目标优化结果较优,可以引导多式联运经营人调整运输方案,减少二氧化碳的排放量,为物流企业开展多式联运运输提供可供参考的依据。

In the development of modern logistics,the timeliness and cost of multimodal transport are factors that cannot be ignored.Aiming at the transportation goal of highway-railway-waterway multimodal transport,this paper mainly studies the path optimization problem of green multimodal transport when the dual uncertainties of transportation time and transit time follow random distribution.The multi-objective optimization model of green multimodal transport path under the dual uncertainties of transportation time and transit time is established with transportation time,carbon emissions and transportation cost as objective functions and carbon emissions as constraints.Accordingly,fuzzy adaptive genetic algorithm and fast non-dominated sorting genetic algorithm(NSGA-Ⅱ) are used to design the multimodal transport path optimization strategy.Finally,the path data from Nanchang to Berlin is used to verify the effectiveness of the proposed method and the results are compared and analyzed.This paper finds that the multi-objective optimization results based on NSGA-Ⅱ algorithm are better,which can guide the multimodal operators to adjust the transportation plans and reduce the emission of carbon dioxide,providing reference for logistics enterprises to carry out multimodal transportation.