Dynamic space deployment model of container storage yard
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摘要: 为了优化集装箱堆场空间资源, 减少集卡行驶距离, 运用数学规划方法, 从平衡箱区倍位箱量和最小化集卡行驶距离入手, 在滚动计划的基础上, 建立了集装箱堆场空间资源动态配置模型。与集装箱随机配置模型相比, 发现集装箱在动态模型配置下, 可使集卡行驶距离减少20%左右, 提高了集卡使用效率, 降低了集装箱作业成本。Abstract: In order to optimize the space of container storage yard and reduce vehicle travel distance, mathematics planning method was applied, the balancing quantity of container and the minimum travel distance of vehicle were considered, a dynamic space deployment model of container storage yard was set up on the base of rolling plan, and the example comparison analysis of the model and random deployment method was done. Analysis result shows that vehicle travel distance decreases by about 20%, vehicle travel efficiency improves, and container operating cost reduces, so the model is feasible.
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Key words:
- traffic planning /
- container /
- resource allocation /
- optimization
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图 3 堆场空间资源配置解决方案
Figure 3. Solving deployment plan of space resource for container yard
表 1 模型参数
Table 1. Model parameters
参数说明 对D型箱 对G型箱 dijl/m 从箱区i的倍位j到泊位l的距离 从箱区i的倍位j到泊位l的距离 Nlt/TEU 在第t阶段从停靠泊位l处船上卸下的D型箱的总箱量 在第t阶段到达码头, 并将装在通过停靠在泊位l处的船上的G型箱的总箱量 M/个 泊位数量 泊位数量 Uijt/TEU Dijt Gijt Xijlt/TEU 在第t阶段从停靠在泊位l处的船上卸下, 存放到箱区i的倍位j中的D型箱量 在第t阶段到码头, 存放到i箱区的j倍位中, 并通过停靠在泊位l处的船舶装上船的G型箱的箱量 
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表 2 箱量Vij1
Table 2. Container quantity Vij1
TEU Vij1 V111 V121 V131 V141 V151 箱量 4 4 6 5 5
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表 3 箱量P
Table 3. Container quantity P
TEU P t=1 t=2 t=3 t=4 t=5 t=6 i=1, j=1 2 0 0 0 2 0 i=1, j=2 0 2 0 0 0 0 i=1, j=3 0 0 0 0 2 0 i=1, j=4 2 0 0 0 0 2 i=1, j=5 0 0 0 0 0 0
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表 4 箱量L
Table 4. Container quantity L
TEU L t=1 t=2 t=3 t=4 t=5 t=6 i=1, j=1 0 0 0 0 0 0 i=1, j=2 0 2 0 0 0 0 i=1, j=3 0 0 0 0 3 0 i=1, j=4 0 0 0 0 0 1 i=1, j=5 0 2 0 0 0 0
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表 5 箱量Gtk
Table 5. Container quantity Gtk
TEU Gtk k=0 k=1 k=2 k=3 k=4 k=5 t=1 10 9 8 7 7 9 t=2 9 6 8 7 8 t=3 9 7 7 9 t=4 10 9 8 t=5 9 8 t=6 9
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表 6 箱量Dtk
Table 6. Container quantity Dtk
TEU Dtk k=0 k=1 k=2 k=3 k=4 k=5 t=1 9 8 8 8 7 7 t=2 8 7 6 8 8 t=3 17 6 8 5 t=4 6 9 7 t=5 9 8 t=6 19
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表 9 距离dijl
Table 9. Distance dijl
m dijl i=1, j=1 i=1, j=2 i=1, j=3 i=1, j=4 i=1, j=5 l=1 200 150 200 250 300 l=2 300 250 200 150 200
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表 10 D型箱的总箱量Nlt
Table 10. Total container quantity Nltof D type
TEU Nlt t=1 t=2 t=3 t=4 t=5 t=6 l=1 30 24 22 18 14 14 l=2 18 13 16 12 9 10
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表 11 G型箱的总箱量Nlt
Table 11. Total container quantity Nltof G type
TEU Nlt t=1 t=2 t=3 t=4 t=5 t=6 l=1 26 22 19 30 13 9 l=2 28 19 22 5 12 5
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表 12 D型箱量Xijlt
Table 12. Container quantity Xijltof D type
TEU Xijlt t=1 t=2 t=3 t=4 t=5 t=6 i=1, j=1, l=1 8 13 0 11 9 4 i=1, j=2, l=1 19 10 13 2 0 0 i=1, j=3, l=1 2 1 8 0 5 10 i=1, j=4, l=1 1 0 0 0 0 0 i=1, j=5, l=1 0 0 1 5 0 0 i=1, j=1, l=2 0 0 0 0 0 0 i=1, j=2, l=2 0 0 0 0 0 0 i=1, j=3, l=2 0 10 0 0 0 2 i=1, j=4, l=2 11 3 1 9 9 7 i=1, j=5, l=2 7 0 15 3 0 1
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表 13 G型箱的箱量Xijlt
Table 13. Container quantity Xijltof G type
TEU Xijlt t=1 t=2 t=3 t=4 t=5 t=6 i=1, j=1, l=1 11 3 17 2 3 0 i=1, j=2, l=1 2 8 0 11 10 5 i=1, j=3, l=1 13 10 2 12 0 1 i=1, j=4, l=1 0 1 0 3 0 0 i=1, j=5, l=1 0 0 0 2 0 3 i=1, j=1, l=2 0 0 0 0 0 0 i=1, j=2, l=2 0 0 0 0 2 0 i=1, j=3, l=2 3 0 4 0 3 0 i=1, j=4, l=2 8 11 17 0 0 0 i=1, j=5, l=2 17 8 1 5 7 5
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表 14 随机试验配置结果
Table 14. Stochastic experiment disposition result
随机分配序号 1 2 3 4 5 6 7 8 距离/m 89 900 98 090 88 698 97 690 95 678 98 790 100 450 90 190
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