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LEI Ding-you, YOU Wei, ZHANG Ying-gui, PI Zhi-dong. Path optimization model and algorithm of multimodal transport for long and bulky cargo[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 75-83.
Citation: LEI Ding-you, YOU Wei, ZHANG Ying-gui, PI Zhi-dong. Path optimization model and algorithm of multimodal transport for long and bulky cargo[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 75-83.
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Path optimization model and algorithm of multimodal transport for long and bulky cargo

  • School of Traffic and Transportation Engineering, Central South University, Changsha 410004, Hunan, China

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  • Author Bio:

    LEI Ding-you(1958-), male, professor, PhD, +86-731-82655931, ding@csu.edu.cn

  • Received Date: 2013-10-11
  • Publish Date: 2014-02-25
    Abstract
  • Based on the view of feasibility and rationality, the path optimization influence factors of multimodal transport for long and bulky cargo were analyzed. The minimum transport time, mileage and cost were taken as objective functions, the line boundary, bridge bearing capacity and lifting equipment capacity were taken as constraint conditions, and the original path optimization model of multimodal transport for long and bulky cargo was set up. By considering the transformation characteristics of constraint conditions, the original model was extended and optimized, two-dimensional sequence coding strategy was designed, and the extensional model was solved by using genetic algorithm. Calculation result shows that by using the extensional model and genetic algorithm, the optimal transport time, mileage and cost are 12.5 d, 1 116 km and 581 800 yuan respectively. By using the extensional model and simulated annealing algorithm, the optimal transport time, mileage and cost are 15.5 d, 1 131 km and 677 400 yuan respectively. By using single-railway transport mode and genetic algorithm, the optimal transport time, mileage and cost are 12.7 d, 1 152 km and 565 000 yuan respectivelg. By using the extensional model and genetic algorithm, the integrated optimization degree rises by 52.22% and 8.95% compared with the other 2 conditions. Obviously, the extensional model is feasible, and genetic algorithm is effective.

     

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