Path optimization model and algorithm of multimodal transport for long and bulky cargo
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摘要: 基于可行性与合理性的角度, 分析了长大货物多式联运路径优化的影响因素。以最小运输时间、里程与费用为目标函数, 以线路限界、桥梁承载能力、起重设备的起重能力为约束条件, 建立了长大货物多式联运路径优化原始模型。考虑了约束条件的改造性特征, 将原始模型扩展优化, 设计了二维序列编码策略, 运用遗传算法求解扩展模型。计算结果表明: 运用提出的优化模型与遗传算法, 最优运输时间、里程和费用分别为12.5d、1 116km、58.18万元, 运用提出的优化模型与模拟退火算法, 最优运输时间、里程和费用分别为15.5d、1 131km、67.74万元; 运用单一的铁路运输方式与遗传算法, 最优运输时间、里程和费用分别为12.7d、1 152km、56.50万元。与其他2种情况比较, 提出的优化模型与遗传算法的综合优化程度分别提高52.22%与8.95%, 可见, 模型可行, 算法有效。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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