【摘要】 集装箱在海铁联运过程中容易受到各种不确定因素的影响，导致运输时间波动，进而影响货物的送达准点率。为有效降低不确定运输时间的影响，兼顾运输过程的经济性和绿色可持续性优化集装箱海铁联运箱流径路。采用随机机会约束规划构建运输总费用最少和碳排放量最低的多目标模型。在约束条件中引入铁路和海洋期望运到时间，并对超过期望运到时间的径路进行惩罚处理，保证运输径路的优越性。考虑一站直达和中转换装这2种运输组织模式，克服现有研究未考虑货源是否充足的缺陷。运用不确定及概率论相关理论知识将不确定约束转化为线性约束。以西安至洛杉矶的集装箱货物出口径路优化为案例背景，采用NSGA-Ⅱ算法求解，并通过贪心算法改进初始化种群以及基于logistics分布的概率选择算子改进精英选择算子。通过对比分析得到以下结果：(1)算法优化后运输总费用减少23.15万美元，碳排放减少6.69 t，同时算法求解速度提高了75.36%；(2)将本文模型选用的随机规划和模糊规划进行对比，发现随机规划解集数量多于模糊规划，且二者在相同输送径路中的运输总费用和碳排放量均优化了10.65%。因此本文模型和算法具有良好的优化效果。进行灵敏度分析，观察置信水平以及时间影响系数对目标函数和货物送达准点率的影响。结果表明：(1)较高的铁路和海洋运输置信水平会提高货物的运输总费用。(2)时间影响系数和货物送达准点率呈负相关，影响系数越大货物送达准点率越低。更多还原

【Abstract】 In the process of sea-rail intermodal transportation, containers are subject to various uncertainties, resulting in time fluctuation, which affects cargo delivery punctuality. To effectively reduce the impact of uncertain transportation time, the economics and green sustainability of the transportation process are considered to optimize the container flow path of sea-rail intermodal transport. A multi-objective model with the least total transportation costs and the lowest carbon emissions is established by stochastic chance-constrained programming. Rail and ocean expected arrival times are introduced into the constraint conditions. And the paths that exceed the expected arrival times are penalized to ensure the superiority of the transportation paths. Consider two modes of transportation organization: one-stop direct delivery and intermediate loading, to overcome the shortcomings of existing studies that do not consider the adequacy of cargo sources. The uncertainty constraints are transformed into linear constraints using the knowledge of uncertainty and probability theory-related theories. The NSGA-II algorithm is used to solve the problem of container cargo outbound route optimization from Xi’an to Los Angeles. The initialization population is improved by the greedy algorithm and the elite selection operator is improved by the probabilistic selection operator based on logistics distribution. The following results are obtained through comparative analysis:(1)The total costs of transportation are reduced by $231 500 and carbon emissions by 6.69 t after algorithm optimization, while the speed of algorithm solution is increased by 75.36%.(2)By comparing the fuzzy programming with the stochastic programming chosen for the model in this paper, it is found that the number of stochastic programming solution sets is more than the fuzzy programming. The total transportation costs and carbon emissions in the same transport path for both are optimized by 10.65% in the stochastic programming. Therefore, the model and algorithm in this paper have positive optimization effects. Finally, sensitivity analysis is performed to observe the impact of confidence level as well as time influence coefficient on the objective function and cargo delivery punctuality. The results show that:(1)Higher levels of confidence in rail and sea transport will increase the total costs of transporting goods.(2) Time impact factor and cargo delivery on-time rate are negatively correlated. The higher the impact factor, the lower the cargo delivery on-time rate.更多还原