Volume 40 Issue 6
Dec.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(6): 106-117
YUAN Xueli, YANG Juhua, REN Jinhui. A Path Optimization Method for Sea-Rail Intermodal Container Transport Under Random Transit Time[J]. Journal of Transport Information and Safety, 2022, 40(6): 106-117. doi: 10.3963/j.jssn.1674-4861.2022.06.011
Citation: YUAN Xueli, YANG Juhua, REN Jinhui. A Path Optimization Method for Sea-Rail Intermodal Container Transport Under Random Transit Time[J]. Journal of Transport Information and Safety, 2022, 40(6): 106-117. doi: 10.3963/j.jssn.1674-4861.2022.06.011
shu

A Path Optimization Method for Sea-Rail Intermodal Container Transport Under Random Transit Time

doi: 10.3963/j.jssn.1674-4861.2022.06.011
  • 1.

    School of Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • 2.

    Safety Production Department of Lanzhou Freight Center, China Railway Lanzhou Bureau Group Company, Limited, Lanzhou 730030, China

  • Received Date: 2022-04-16
    Available Online: 2023-03-27
    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: ①The 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%. ②By 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: ①Higher 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.

     

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