集装箱码头泊位-堆场-闸口的周期协同分配

韩笑乐; 鞠留红; 栾晨; 陆志强

doi:10.3969/j.issn.0258-2724.20170924

集装箱码头泊位-堆场-闸口的周期协同分配

doi: 10.3969/j.issn.0258-2724.20170924

基金项目: 国家自然科学基金资助项目（71502129，61473211）

详细信息

作者简介:
韩笑乐（1983—），男，讲师，博士，研究方向为集装箱码头运营优化，E-mail：hanxiaole@tongji.edu.cn

通讯作者:
陆志强（1968—），男，教授，博士生导师，研究方向为物流与供应链管理建模与优化、生产工程，E-mail：zhiqianglu@tongji.edu.cn

中图分类号: U69；O221；TP29
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- 文章访问数: 448
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- 被引次数: 0
出版历程
- 收稿日期: 2017-12-21
- 修回日期: 2018-04-17
- 网络出版日期: 2018-07-08
- 刊出日期: 2019-08-01

Periodic and Collaborative Allocation of Berth-Yard-Gate Resources at Container Terminals

摘要

摘要: 为提高集装箱进出口码头在周期性环境下的作业效率，对集装箱码头泊位-堆场-闸口的周期协同分配问题进行了研究. 首先考虑泊位、堆场、闸口3类资源对船分配过程中的可用量约束、相关性约束和周期性约束，以最小化船舶总在港时间为目标函数，建立集成调度的混合整数规划模型；在此基础上设计自适应遗传算法进行求解，其上层对船舶优先级构成的编码空间展开进化搜索，下层利用启发式将优先级解码为多资源协同分配计划，并将其评价值返回至上层迭代. 数值实验显示，协同考虑泊位、堆场和闸口3类资源的集成调度，相较于传统的两阶段调度，周期计划下的船舶总在港时间缩短约20%.
- 集装箱码头 /
- 资源分配 /
- 周期性 /
- 闸口 /
- 遗传算法
Abstract: In order to improve the efficiency of import/export container terminals in periodic environment, the periodical and collaborative allocation problem of key resources at container terminals, including berth, yard and gate is investigated. A mixed-integer linear programming model is first established for the integrated scheduling of three types of resources. This model takes into account the capacity restrictions, inter-relationships and periodicity requirements, and sets the objective of minimizing total dwelling time of all vessels. Furthermore, an adaptive genetic algorithm is proposed to find a solution. As for the algorithm, the upper level performs evolutionary search within the space consisting the encoded vessel priority lists, while the lower level decodes each vessel priority to generate a complete resource allocation plan by heuristics, and returns its evaluation for upper level iteration. Numeral experiments shows that with the collaborative allocation of berth-yard-gate resources, the dwelling time of vessels in the execution of periodic plan is shortened by 20% comparing to traditional two-stage method.
- container terminal /
- resource allocation /
- periodicity /
- gate /
- genetic algorithm

HTML全文

图 1 泊位-堆场-闸口资源分配相关关系

Figure 1. Inter-relationship among berth-yard-gate resource allocations

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图 2 启发式算法流程

Figure 2. Flow chart of the proposed heuristic algorithm

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图 3 堆场调度安排示例

Figure 3. Illustration of yard resource allocation

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图 4 敏感性分析

Figure 4. Sensitivity analysis

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表 1 船舶类型及其参数

Table 1. Parameters of different types of vessel

船舶类型	船长/（×10 m）	占比	装卸载箱总量/TEU	（预存/留存期）/h
小型	U[10，20]	1/3	U[360，1 080]	72
中型	U[20，30]	1/3	U[960，1 920]	72
大型	U[30，40]	1/3	U[1 800，3 600]	72

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表 2 不同问题规模下的可用资源配置

Table 2. Resource configurations under different problem scales

V/条	J/（×10 m）	S/TEU	${G_{\rm{L}}}$/（TEU•h^–1）	${G_{\rm{D}}}$/（TEU•h^–1）
20	70	28 800	18	18
30	110	38 400	27	27
40	150	57 600	36	36

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表 3 20船规模下数值实验

Table 3. Algorithm performance of 20 vessels

算例	Z	Z₁	Z₂	g/%
1	202	142	229	-11.8
2	258	109	322	-19.8
3	194	96	288	-32.6
4	195	114	242	–19.4
5	224	119	327	–31.5
6	203	116	227	–10.6
7	202	121	262	–22.9
8	170	112	186	–8.6
9	215	128	254	–15.4
10	152	95	187	–22.8
平均值	201.5	115.2	253.4	–20.5

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表 4 30船规模下数值实验

Table 4. Algorithm performance of 30 vessels

算例	Z	Z₁	Z₂	g/%
1	318	197	412	–22.8
2	293	145	384	–23.7
3	268	154	380	–29.5
4	259	140	313	–17.3
5	302	172	381	–20.7
6	277	154	347	–20.2
7	262	146	325	–19.4
8	239	167	256	–6.6
9	330	157	436	–24.3
10	260	168	317	–18.0
平均值	280.8	160	355.1	–19.8

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表 5 40船规模下数值实验

Table 5. Algorithm performance of 40 vessels

算例	Z	Z₁	Z₂	g/%
1	387	251	445	–13.0
2	350	187	443	–21.0
3	349	184	460	–24.1
4	298	187	370	–19.5
5	382	208	505	–24.4
6	284	188	330	–13.9
7	362	201	434	–16.6
8	326	185	401	–18.7
9	402	222	544	–26.1
10	299	183	358	–16.5
平均值	343.9	199.6	429.0	–19.8

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