Method for allocation of loading and unloading equipment in container rail-water intermodal hub
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摘要:
为提高集装箱铁水联运枢纽装卸设备配置效率并降低集卡走行碳排放，本文考虑在“铁-水”直取与“铁-堆场-水”混合装卸模式下箱流与车船到达时空特性，构建了多目标组合优化模型。以设备运营成本、车船周转时间、集卡走行碳排放为优化目标，采用分区域集卡动态分配策略，引入时间离散区段化设备分配机制，动态决策了岸桥、铁路装卸区轨道吊、堆场轮胎吊及集卡的配置数量；基于时段分配规则的遗传算法求解模型，结合某铁水联运枢纽24 h运营数据进行验证。研究结果表明：对于原算例，在车船装卸时间重叠时，车船直取比例达82%，岸桥、铁路装卸区轨道吊与堆场轮胎吊的设备利用率提升了15%~20%，其中出口箱区、铁路进口箱区的轮胎吊作业量分别下降了15%、23%，证明车船直取是铁水联运转运的优选方式，可以显著提升枢纽设备的利用率，且铁路进口箱区堆场轮胎吊受直取能力影响显著；当限制枢纽直取区域内集卡配置量由8台减为2~4台时，总成本上升了1.2%~2.8%，集卡走行碳排放增加了16%，表明总成本与碳排放均对直取能力较为敏感；在装卸规模较大即5船4列时，设备运营成本占总成本的84.12%，当装卸规模减小至1船1列时，设备成本占比升至87.8%，表明动态设备分配机制在较大规模场景下能更有效地协调设备资源；所提算法与MATLAB整数线性规划求解器直接求解相比，枢纽运营总成本降低了19.1%；相较于标准遗传算法，平均求解时间缩短了33.5%，平均解质量提升了37.62%，表明本文算法稳定性与求解精度更好。研究结果可为铁水联运枢纽设备配置提供兼顾效率与环境友好的决策方法，通过动态分配设备数量，提升车船直取能力，减少堆场堆存量。
Abstract:To improve the efficiency of loading and unloading equipment allocation in rail-water intermodal transport container terminals and to reduce carbon emissions generated by container truck operations, a multi-objective combinatorial optimization model was developed considering the spatiotemporal characteristics of container flows and the rail-ship arrival under a hybrid handling mode of rail-water direct transfer and rail-yard-water operation. The optimization objectives were to minimize equipment operating costs, the turnaround times of trains and ships, and carbon emissions from container truck movements. A dynamic zone-based allocation strategy was proposed for container trucks. A discrete time-period equipment allocation mechanism was also introduced. The allocation numbers of quay cranes, rail-mounted gantry cranes, rubber tyred gantry cranes, and container trucks were dynamically determined. A genetic algorithm based on time-period allocation rules was designed to solve the model. The model was validated using 24-hour operational data from a rail-water intermodal transport container terminal. The results indicate that, in the original case, when the loading and unloading time windows of trains and ships overlap, the proportion of rail-ship direct transfer reaches 82%, the utilization rates of quay cranes, rail-mounted gantry cranes, and rubber tyred gantry cranes increase by 15%-20%, and the workload of rubber tyred gantry cranes in the export container yard and railway import container yard decreases by 15% and 23%, respectively. It proves that rail-ship direct transfer is the preferred transshipment mode for rail-water intermodal transport, which can significantly improve the utilization rate of terminal handling equipment, with significant impact of direct transfer capacity on the rubber tyred gantry cranes in railway import container yards. When the number of container trucks allocated to the direct-transfer zone reduces from 8 to 2-4 units, the total cost increases by 1.2%-2.8% and carbon emissions from container truck movements rise by approximately 16%, which indicates that both total cost and carbon emissions are highly sensitive to direct transfer capacity. Furthermore, under a large-scale handling scenario involving 5 ships and 4 trains, equipment operating costs account for 84.12% of the total cost, whereas this proportion increases to 87.8% when the handling scale is reduced to 1 ship and 1 train. This suggests that the proposed dynamic equipment allocation mechanism is more effective in coordinating equipment resources under larger-scale operational conditions. Compared with directly solving the model using the MATLAB integer linear programming solver, the proposed algorithm lowers the total operating cost of the terminal by 19.1%. Compared with the standard genetic algorithm, it shortens the average solution time by 33.5% and improves the average solution quality by 37.62%, which indicates its superior stability and solution accuracy. An efficiency-oriented and environmentally friendly decision-making approach can be provided for equipment configuration in rail-water intermodal transport container terminals. By dynamically allocating equipment quantities, the rail-ship direct transfer capability can be enhanced and yard congestion and storage pressure can be alleviated.
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图 1 铁水联运枢纽内各箱流所经装卸作业
Figure 1. Loading and unloading operations corresponding to various container flows in a rail-water intermodal transport container terminal
图 4 基于时段分配规则的个体更新算法
Figure 4. Individual updating algorithm based on the time period allocation rule
图 7 算例目标函数收敛
Figure 7. Convergence of the objective function of the calculation example
图 8 船舶与列车结束装卸时段分布
Figure 8. Distribution of loading and unloading periods for ships and trains
图 10 出口方向1对应堆场的轮胎吊作业量变化
Figure 10. Changes in the workload of rubber tyred gantry cranes in the yard corresponding to export direction 1
图 11 出口方向2对应堆场的轮胎吊作业量变化
Figure 11. Changes in the workload of rubber tyred gantry cranes in the yard corresponding to export direction 2
图 12 公路进口方向3对应堆场的轮胎吊作业量变化
Figure 12. Changes in the workload of rubber tyred gantry cranes in the yard corresponding to highway import direction 3
图 13 铁路进口方向4对应堆场的轮胎吊作业量变化
Figure 13. Changes in the workload of rubber tyred gantry cranes in the yard corresponding to railway import direction 4
图 14 铁路进口方向5对应堆场的轮胎吊作业量变化
Figure 14. Changes in the workload of rubber tyred gantry cranes in the yard corresponding to railway import direction 5
图 15 总成本随区域3集卡允许配置数量变化
Figure 15. The total cost varies with the allowed configuration quantity of container trucks in the region 3
图 16 区域3集卡在不同的最大允许配置数量下总成本的收敛过程
Figure 16. Convergence process of the total cost for the region 3 container trucks under different maximum allowable configuration quantities
表 1 班轮信息
Table 1. Liner information
班轮序号 到港时间 离港时间 集装箱量/TEU 方向1 方向2 方向3 方向4 方向5 1 2025-01-22 12：05 2025-01-23 01：05 92 139 57 23 44 2 2025-01-22 14：30 2025-01-23 03：30 82 135 102 34 20 3 2025-01-22 22：00 2025-01-23 04：00 90 56 44 12 33 4 2025-01-23 01：00 2025-01-23 06：30 166 52 47 18 54 5 2025-01-23 04：30 2025-01-23 11：00 62 55 161 34 48 
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表 2 班列信息
Table 2. Train information
班列序号 到港时间 离港时间 集装箱量/TEU 方向1 方向2 方向3 方向4 方向5 1 2025-01-22 14：00 2025-01-22 17：00 33 10 0 45 34 2 2025-01-22 16：00 2025-01-22 19：00 22 38 0 18 37 3 2025-01-23 02：00 2025-01-23 05：00 32 39 0 22 50 4 2025-01-23 06：00 2025-01-23 09：00 57 45 0 29 58
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表 3 外集卡信息
Table 3. External container truck information
时段序号 集装箱量/TEU 时段序号 集装箱量/TEU 方向1 方向2 方向3 方向1 方向2 方向3 1 12 12 19 13 14 22 24 2 18 8 17 14 7 8 13 3 25 8 15 15 22 15 6 4 11 20 19 16 13 10 13 5 18 12 14 17 20 9 11 6 25 8 22 18 14 5 25 7 10 12 19 19 8 7 22 8 13 10 17 20 9 15 12 9 7 9 23 21 24 24 16 10 27 10 11 22 10 10 25 11 17 10 22 23 16 10 13 12 27 21 14 24 21 11 19
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表 4 堆场原始箱量
Table 4. Original container volume in the yard
TEU 箱流方向 方向1 方向2 方向3 方向4 方向5 对应堆场箱量 14 19 22 7 20
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表 5 布局信息
Table 5. Layout information
属性 数值 水平方向的箱区个数 10 单个箱区长度/m 130 堆场宽度/m 220 堆场边缘到码头前沿的距离/m 200 堆场边缘到铁路装卸线的距离/m 100 内集卡在区域1平均走行距离/m 1 927 内集卡在区域2平均走行距离/m 1 727 内集卡在区域3平均走行距离/m 2 041
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表 6 装卸区域占用费用
Table 6. Occupation fees of loading and unloading areas
元·h-1 属性 数值 船舶停泊费率 978.5 铁路装卸线占用费率 175.2
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属性 岸桥 场桥 轨道吊 设备总量/台 6 12 5 同时段最大允许配置数量/台 3 4 3 每时段花费成本/元 735 600 700 单位时段实际装卸箱数/（TEU·h-1） 25 14 20 设备利用率 0.8 0.9 0.8
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表 8 各区域内集卡装卸信息
Table 8. Internal container truck loading and unloading information within each region
属性 区域1 区域2 区域3 内集卡平均走行距离/m 1 927 1 727 2 041 最大允许配置数/台 10 8 8 等待时长/min 2 2 1 装卸/对集装箱所需时长/min 7.5 6.9 6.8 单位时段可装卸箱数/（TEU·h-1） 15 17 17 单位时段碳排放所需碳税/（元·h-1） 9.2 10.4 10.4 单位时段装卸成本/（元·h-1） 217.5 246.5 246.5
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表 9 各运输单元的装卸时间窗
Table 9. Loading and unloading time windows for each transportation unit
运输工具 序号 开始装卸时段 结束装卸时段 最晚结束装卸时段 船舶 V1 1 9 14 V2 3 11 16 V3 11 15 17 V4 14 19 19 V5 17 24 24 列车 K1 3 5 6 K2 5 8 8 K3 15 18 18 K4 19 22 22
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表 10 船舶与列车在各时段被分配的起重设备数量
Table 10. Number of cranes allocated to ships and trains at each time period
时段序号 不同运输工具被分配的起重设备数量/台 V1 V2 V3 V4 V5 K1 K2 K3 K4 1 1 2 3 3 3 3 2 4 2 3 2 5 2 3 2 6 1 1 1 7 2 8 1 1 3 9 1 3 10 2 11 2 1 12 2 13 3 14 2 2 15 2 3 1 16 3 3 17 3 1 3 18 2 2 1 19 2 3 2 20 3 3 21 2 2 22 2 3 23 2 24 1
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表 11 箱区与区域在各时段被分配的起重设备和集卡数量
Table 11. Number of cranes or container trucks allocated to the container block and region at each time period
时段序号 箱区轮胎吊分配数量/台 集卡走行区集卡分配数量/台 箱区1 箱区2 箱区3 箱区4 箱区5 区域1 区域2 区域3 1 2 4 3 1 2 2 2 4 3 3 5 3 3 3 3 3 8 2 2 4 3 4 2 5 6 5 4 3 1 2 3 4 6 3 3 1 4 1 1 7 1 3 2 3 8 4 3 3 3 4 9 4 4 2 6 10 2 3 4 4 11 3 2 4 5 12 4 2 1 4 13 3 3 2 1 4 14 3 3 4 1 7 15 4 4 2 6 1 2 16 4 2 2 1 3 5 17 4 2 3 5 1 5 18 4 1 1 2 1 3 19 4 4 3 7 3 20 3 2 4 5 1 3 21 4 2 3 3 3 22 3 2 4 3 4 23 2 1 4 3 24 2 1 3 2
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表 12 不同装卸规模算例的信息及计算结果
Table 12. Information and calculation results for cases with different handling scales
装卸规模 总任务箱量/TEU 总成本/元 设备装卸成本/元 泊位及装卸线占用成本/元 碳排放成本/元 箱均成本/元 5船4车（原算例） 1 660 55 835.60 46 971.0 7 972.8 891.8 33.64 3船3车 1 089 36 349.13 31 291.3 4 460.1 597.7 33.38 1船1车 573 19 100.20 16 769.3 2 062.1 268.7 33.33
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表 13 算法求解总成本的性能对比
Table 13. Performance comparison of algorithms for solving total cost cost
性能指标 标准遗传算法 本文改进的遗传算法 最优总成本/元 55 835.6 55 835.6 平均求解时间/s 45.20 30.04 平均总成本/元 91 104.08 56 830.71 标准差/元 12 450.68 7 832.88 中位数/元 92 400.28 57 084.68 最差解/元 112 300.41 68 920.39
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