随机运输时间下集装箱海铁联运箱流径路优化方法

袁雪丽; 杨菊花; 任金荟

doi:10.3963/j.jssn.1674-4861.2022.06.011

随机运输时间下集装箱海铁联运箱流径路优化方法

doi: 10.3963/j.jssn.1674-4861.2022.06.011

1.
兰州交通大学交通运输学院兰州 730070
2.
中国铁路兰州局集团有限公司兰州货运中心安全生产部兰州 730030

基金项目:

国家自然科学基金项目 62141303

甘肃省自然科学基金项目 21JR7RA287

中央引导地方发展资金项目 22ZY1QA005

详细信息

作者简介:
袁雪丽(1996—), 硕士研究生.研究方向: 集装箱多式联运.E-mail: yuan102356@163.com

通讯作者:
杨菊花(1978—), 博士, 教授.研究方向: 运输规划与管理.E-mail: yangjuhua@mail.lzjtu.cn

中图分类号: U116.2;U169.6+2
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出版历程
- 收稿日期: 2022-04-16
- 网络出版日期: 2023-03-27

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

YUAN Xueli^1
,,
YANG Juhua^{1
, ,},
REN Jinhui²

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

摘要

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。
- 海铁联运 /
- 运输径路优化 /
- 碳排放 /
- 不确定模型 /
- NSGA-Ⅱ算法
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.
- sea-rail intermodal transport /
- transportation route optimization /
- carbon emissions /
- uncertain model /
- NSGA-Ⅱ algorithm

HTML全文

图 1 箱流流通方向图

Figure 1. Container flow direction diagram

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图 2 算法流程图

Figure 2. Algorithm flow chart

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图 3 交叉示意图 1

Figure 3. Cross diagram 1

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图 4 交叉示意图 2

Figure 4. Cross diagram 2

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图 5 变异示意图

Figure 5. Variation diagram

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图 6 西安至洛杉矶港运输线路示意图

注：如318/1.23/44代表西安至侯马的铁路区间距离/电力机车单耗系数/铁路区间通过能力；13 507/0/0代表青岛港到洛杉矶港的海洋运输距离；378代表西安车站接发列车能力。

Figure 6. Schematic diagram of transportation route from Xi'an to Los Angeles port

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图 7 运输总费用收敛情况比较

Figure 7. Comparison of total transportation cost convergence

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图 8 碳排放量收敛情况比较

Figure 8. Comparison of carbon emissions convergence

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图 9 单双目标对比分析

Figure 9. Comparative analysis of single and dual targets

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图 10 不同置信水平下的运输费用图

Figure 10. Transportation cost under different confidence levels

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图 11 时间影响系数对货物送达准点率的影响

Figure 11. Influence of time influence coefficient on on-time delivery of goods

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表 1 船舶类型表

Table 1. Ship type table

船舶类型	平均运行速度/（km/h）	海上包箱费率/（＄/TEU·km）	载运能力/（TEU）
船舶1	48	0.20	7 800
船舶2	55	0.23	7 000
船舶3	45	0.17	8 000

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表 2 集装箱港站换装存储能力表

Table 2. Table of container port station reloading storage capacity

集装箱港站	换装能力/（TEU/d）	存储能力/TEU
青岛港	24 000	3 000
上海港	26 000	3 800

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表 3 相关参数说明表

Table 3. Description of relevant parameters

参数名称	参数取值	参数名称	参数取值
c₁ /（＄/列）	6 280	ζ / (＄/TEU·h)	2.03
c₂ /（＄/TEU·km）	0.39	ϕ /（kW·h/t·km）	0.013
c₃ /（＄/h）	7.85	μ/（kg/t·km）	0.002
c₄ /（＄/TEU）	2.36	T_e¹ /(h)	100
η /（TEU/h）	100	T_e² /(h)	300
y_s /（＄/艘）	785	θ	0.01

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表 4 运输方案Pareto解集

Table 4. The pareto solution set of transportation scheme

运输组织模式	方案种类	运输径路	运输总费用/＄	碳排放量/kg	货物送达准点率/%
一站直达式	1	西安-洛阳-郑州-商丘-徐州-济南-青岛港（运输线路m₁）-船舶3-洛杉矶港	9 605 176.17	6 614 632.03	100
	2	西安-洛阳-郑州-商丘-徐州-蚌埠-南京-上海港（运输线路m₂）-船舶3-洛杉矶港	9 638 278.24	6 592 891.81	100
	3	西安-侯马-太原-石家庄-衡水-济南-青岛港（运输线路m₃）-船舶3-洛杉矶港	9 676 218.62	6 696 418.56	100
中转换装式	4	西安-洛阳-郑州-商丘-徐州-济南-青岛港（运输线路m₁）-船舶3-洛杉矶港	10 096 554.48	6 600 204.75	63
	5	西安-洛阳-郑州-商丘-徐州-蚌埠-南京-上海港（运输线路m₂）-船舶3-洛杉矶港	10 136 002.52	6 578 812.57	59
	6	西安-洛阳-郑州-商丘-合肥-南京-上海港（运输线路m₃）-船舶3-洛杉矶港	9 691 412.956	6 627 294.87	100

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表 5 算法改进前后结果对比分析

Table 5. Comparative analysis of results before and after algorithm improvement

算法	运输总费用均值/＄	碳排放量均值/kg	收敛代数均值/代
改进NSGA-Ⅱ算法	9 900 047.14	6 625 682.69	74.40
NSGA-Ⅱ算法	10 131 505.87	6 692 570.01	302.00

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表 6 确定环境和不确定环境的对比分析

Table 6. Comparative analysis of deterministic and uncertain environments

运输组织模式	方案种类	运输费用/＄		费用相对偏差	货物送达准点率/%
运输组织模式	方案种类	确定环境下	不确定环境下	费用相对偏差	确定环境下	不确定环境下
一站直达式	1	9 605 175.93	9 605 176.17	2.50×10^-8	100	100
	2	9 638 229.39	9 638 278.24	5.07×10^-6	100	100
	3	9 676 217.55	9 676 218.62	1.11×10^-7	100	100
中转换装式	4	10 090 676.78	10 096 554.48	5.82×10^-4	100	63
	5	10 127 743.82	10 136 002.52	8.15×10^-4	100	59
	6	9 691 369.68	9 691 412.956	4.47×10^-6	100	100

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表 7 随机规划与模糊规划对比分析

Table 7. Comparison analysis of stochastic planning and fuzzy planning

规划类型	运输方案Pareto解集	运输总费用（＄）
随机规划	西安-侯马-太原-石家庄-衡水-济南-青岛港-船舶3-洛杉矶港	9 676 234.51
	西安-洛阳-郑州-商丘-合肥-南京-上海港-船舶3-洛杉矶港	9 691 400.36
	西安-洛阳-郑州-商丘-徐州-济南-青岛港-船舶3-洛杉矶港	10 093 651.87
模糊规划	西安-洛阳-郑州-商丘-徐州-蚌埠-南京-上海港-船舶3-洛杉矶港	10 137 399.31
	西安-侯马-太原-石家庄-衡水-济南-青岛港-船舶1-洛杉矶港	10 829 585.35
	西安-洛阳-郑州-商丘-合肥-南京-上海港-船舶1-洛杉矶港	10 846 558.88

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