面向大型岛屿的陆海联合跨海应急投送选址-路径优化研究

李嘉诚; 吴迪; 王锋; 刘保利; 郑建风

doi:10.19818/j.cnki.1671-1637.2026.119

面向大型岛屿的陆海联合跨海应急投送选址-路径优化研究

doi: 10.19818/j.cnki.1671-1637.2026.119

李嘉诚^1,,
吴迪^1, ,,
王锋²,
刘保利¹,
郑建风¹

1.
大连海事大学交通运输工程学院，辽宁大连 116026
2.
上海船舶研究设计院船舶设计三部，上海 201203

基金项目:

国家自然科学基金项目 72104042

国家自然科学基金项目 72301051

详细信息

作者简介:
李嘉诚(2000-)，男，辽宁兴城人，工学博士研究生，E-mail：lijc@dlmu.edu.cn

通讯作者:
吴迪(1989-)，男，黑龙江大庆人，副教授，工学博士，E-mail：wudidlmu@163.com

中图分类号: U491.1
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- 文章访问数: 13
- HTML全文浏览量: 13
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-22
- 录用日期: 2025-11-27
- 修回日期: 2025-09-22
- 刊出日期: 2026-06-28

Location-routing optimization for joint land-sea emergency delivery to large islands

1.
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
2.
Ship Design Department 3, Shanghai Merchant Ship Design & Research Institute, Shanghai 201203, China

Funds:

National Natural Science Foundation of China 72104042

National Natural Science Foundation of China 72301051

More Information

Corresponding author: WU Di, associate professor, PhD, E-mail: wudidlmu@163.com

Article Text (Baidu Translation)

摘要

摘要: 综合考虑物资优先级、船队异质性及集结港与船舶的人员/物资容量等现实因素，构建了以集结港选址、运输船舶航次和航线配置为变量，以投送时间最短为目标的选址-路径优化模型；提出了一种集成优化算法，以自适应大邻域搜索算法为框架构成外层优化循环，通过嵌套改进模拟退火算法形成内部优化模块，并通过内外层交互机制实现全局与局部搜索的融合；为验证模型与算法的有效性，以向中国南海地区应急投送为例进行实证研究。研究结果表明：优化后投送时间由78.52 h缩短至48.03 h，较现有算法效率提升6.54%~48.51%，稳定性提高10.77%~72.92%；进一步敏感性分析发现，船舶容量、速度、集结港数量和陆上运输平均速度均与系统投送时间负相关，而投送人员与物资数量和登陆港数量均与系统投送时间正相关，边际贡献递减，小容量、低速船舶的部署会显著削弱系统性能。所提算法能有效平衡多货种优先级冲突与异质运输资源限制，可为陆海联合跨海应急投送提供兼具时效性与鲁棒性的决策支持，对特殊场景下的选址-路径问题研究具有理论拓展价值。
- 交通规划 /
- 应急投送 /
- 自适应大邻域搜索算法 /
- 选址-路径优化 /
- 陆海联运 /
- 航线配置
Abstract: Considering some real-world factors, such as material priority, fleet heterogeneity, and the personnel/material capacities of assembly ports and vessels, a location-routing optimization model was constructed. This model employed assembly port location, vessel voyage scheduling, and route configuration as variables, aiming to minimize delivery time. An integrated optimization algorithm was proposed. It leveraged an adaptive large neighborhood search algorithm as the framework for the outer-layer optimization loop. An improved simulated annealing algorithm was embedded as the inner-layer optimization module. The integration of global and local search was achieved through an interaction mechanism between the inner and outer layers. The effectiveness of the proposed model and algorithm was validated through an empirical case study of emergency delivery to China's South China Sea region. Research results show that the optimized delivery time is reduced from 78.52 h to 48.03 h, showing an efficiency improvement of 6.54%-48.51% and a stability enhancement of 10.77%-72.92% in comparison with existing algorithms. Further sensitivity analysis reveals that vessel capacity, speed, the number of assembly ports, and the average speed of ground transportation are all negatively correlated with delivery time of the system, while delivery personnel and material quantities, the number of landing ports are all positively correlated with delivery time of the system, exhibiting diminishing marginal effects. Deploying small or slow vessels can significantly undermine system performance. The proposed algorithm has the capability of effectively balancing material priority conflicts and heterogeneous transportation resource constraints. It provides timely and robust decision support for joint land-sea emergency delivery, and offers theoretical implications for extending the research on location-routing problems in special scenarios.
- transportation planning /
- emergency delivery /
- adaptive large neighborhood search algorithm /
- location-routing optimization /
- joint land-sea transport /
- route configuration

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图 1 算法流程

Figure 1. Algorithm flow

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图 2 解的表达式

Figure 2. Solution expression

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图 3 交换算子

Figure 3. Exchange operator

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图 4 随机替换算子

Figure 4. Random replacement operator

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图 5 贪婪替换算子

Figure 5. Greedy replacement operator

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图 6 翻转算子

Figure 6. Inversion operator

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图 7 优先级表达式

Figure 7. Priority expression

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图 8 算法运行过程

Figure 8. Algorithm execution process

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图 9 初始阶段与迭代阶段应急投送方案

Figure 9. Emergency delivery schemes at the initial and iterative stages

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图 10 收敛阶段应急投送方案

Figure 10. Emergency delivery scheme at the convergence stage

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图 11 不同调度方案的对比

Figure 11. Comparison of different preceding scheduling schemes

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图 12 各场景下算法收敛过程

Figure 12. Algorithm convergence processes under different scenarios

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图 13 船舶容量与速度的交互灵敏度分析

Figure 13. Sensitivity analysis of the interaction between ship capacity and speed

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图 14 投送人员与物资数量灵敏度分析

Figure 14. Sensitivity analysis of delivery personnel and material quantities

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图 15 集结港数量灵敏度分析

Figure 15. Sensitivity analysis of assembly port numbers

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图 16 登陆港数量灵敏度分析

Figure 16. Sensitivity analysis of landing port numbers

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图 17 陆上运输平均速度灵敏度分析

Figure 17. Sensitivity analysis of the average speed of ground transportation

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表 1 船舶容量

Table 1. Ship loading capacity

船舶编号	1#	2#	3#	4#	5#	6#	7#	8#	9#	10#
载人量/人	4 000	3 800	3 600	3 400	3 500	3 200	2 800	2 600	2 400	2 200
载货量/t	3 600	3 400	3 200	3 000	3 000	2 800	2 600	2 400	2 200	2 000

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表 2 各保障基地人员与物资数量

Table 2. Numbers of personnel and materials at each support base

保障基地	广州	茂名	韶关	云浮	湛江	南宁	桂林	玉林	百色	钦州
人员数	2 500	0	0	0	0	2 500	4 000	0	0	0
第1类物资/t	2 000	0	0	0	0	4 000	3 200	0	0	0
第2类物资/t	0	1 200	1 200	0	0	0	0	2 000	1 000	2 000
第3类物资/t	0	0	0	600	600	0	0	0	0	0

保障基地	贵阳	毕节	遵义	铜仁	六盘水	长沙	邵阳	衡阳	怀化	永州
人员数	0	0	6 000	0	6 000	0	0	0	0	4 000
第1类物资/t	0	0	4 800	0	4 800	0	0	0	0	3 200
第2类物资/t	0	0	0	0	0	1 500	2 000	0	1 500	0
第3类物资/t	1 000	1 500	0	1 000	0	0	0	1 500	0	0

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表 3 各登陆港人员与物资需求数量

Table 3. Numbers of personnel and materials at each landing port

登陆港	海口港	洋浦港	龙湾港	三亚港
人员数	8 500	6 500	6 000	4 000
第1类物资/t	6 800	5 400	4 800	3 000
第2类物资/t	4 200	3 200	3 000	2 000
第3类物资/t	2 100	1 600	1 500	1 000

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表 4 各保障基地至集结港距离

Table 4. Distances from each support base to the assembly port km

保障基地	广州港	茂名港	海安新港	北海港	防城港
广州	82	316	546	550	641
茂名	350	46	220	234	327
韶关	289	510	724	716	792
云浮	200	226	439	423	503
湛江	430	109	138	179	272
南宁	607	414	431	222	148
桂林	536	532	643	517	514
玉林	420	203	295	196	275
百色	831	638	644	434	361
钦州	615	315	321	112	56
贵阳	985	908	970	761	687
毕节	1 161	1 084	1 146	936	863
遵义	1 118	1 039	1 101	892	818
铜仁	927	947	1 035	875	845
六盘水	1 208	1 106	1 111	902	828
长沙	733	919	1 094	1 005	998
邵阳	635	746	915	795	786
衡阳	576	755	927	836	829
怀化	811	856	966	809	779
永州	543	650	820	728	723

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表 5 各港之间距离

Table 5. Distances between ports n mile

港口	广州港	茂名港	海安新港	北海港	防城港	海口港	洋浦港	龙湾港	三亚港
广州港	0	170	255	362	398	258	320	269	331
茂名港	170	0	97	192	238	103	164	142	240
海安新港	255	97	0	103	140	15	79	100	200
北海港	362	192	103	0	46	109	102	201	231
防城港	398	238	140	46	0	145	123	236	239
海口港	258	103	15	109	145	0	66	99	193
洋浦港	320	164	79	102	123	66	0	171	142
龙湾港	269	142	100	201	236	99	171	0	104
三亚港	331	240	200	231	239	193	142	104	0

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表 6 第1航次船舶海上运输方案

Table 6. Maritime transportation plan of vessels for the 1st voyage

船舶编号	航线	港口作业说明
1#	防城港→洋浦港	防城港装载4 000人、3 600 t第1类物资；洋浦港卸载4 000人、3 600 t第1类物资
2#	防城港→洋浦港→海口港	防城港装载3 800人、3 400 t第1类物资；洋浦港卸载2 500人、1 800 t第1类物资；海口港卸载1 300人、1 600 t第1类物资
3#	防城港→海安新港→海口港	防城港装载1 200人、200 t第1类物资；海安新港装载2 400人、3 000 t第1类物资；海口港卸载3 600人、3 200 t第1类物资
4#	北海港→海安新港→海口港→龙湾港	北海港装载3 400人、1 800 t第1类物资；海安新港装载1 200 t第1类物资；海口港卸载3 400人、2 000 t第1类物资；龙湾港卸载1 000 t第1类物资
5#	北海港→海安新港→海口港→龙湾港	北海港装载200人；海安新港装载3 300人、3 000 t第1类物资；海口港卸载200人；龙湾港卸载3 300人、3 000 t第1类物资
6#	海安新港→龙湾港→三亚港	海安新港装载3 200人、2 800 t第1类物资；龙湾港卸载2 700人、800 t第1类物资；三亚港卸载500人、2 000 t第1类物资
7#	北海港→海安新港→洋浦港→三亚港	北海港装载1 500 t第2类物资；海安新港装载2 800人、1 000 t第1类物资和100 t第2类物资；洋浦港卸载1 600 t第2类物资；三亚港卸载2 800人、1 000 t第1类物资
8#	海安新港→洋浦港→海口港→三亚港	海安新港装载700人、2 400 t第2类物资；洋浦港卸载1 600 t第2类物资；海口港卸载800 t第2类物资；三亚港卸载700人
9#	海安新港→海口港	海安新港装载2 200 t第2类物资；海口港卸载2 200 t第2类物资
10#	海安新港→茂名港→海口港→龙湾港	海安新港装载1 500 t第2类物资；茂名港装载500 t第2类物资；海口港卸载1 200 t第2类物资；龙湾港卸载800 t第2类物资

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表 7 第2航次船舶海上运输方案

Table 7. Maritime transportation plan of vessels for the 2nd voyage

船舶编号	航线	港口作业说明
1#	洋浦港→茂名港→洋浦港→三亚港	洋浦港返回茂名港；茂名港装载2 000 t第2类物资、1 600 t第3类物资；洋浦港卸载1 600 t第3类物资；三亚港卸载2 000 t第2类物资
2#	茂名港→龙湾港→三亚港	茂名港装载1 400 t第3类物资；龙湾港卸载400 t第3类物资；三亚港卸载1 000 t第3类物资
3#	茂名港→海口港→龙湾港	茂名港装载3 200 t第3类物资；海口港卸载2 100 t第3类物资；龙湾港卸载1 100 t第3类物资
9#	茂名港→龙湾港	茂名港装载2 200 t第2类物资；龙湾港卸载2 200 t第2类物资

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表 8 算法结果对比

Table 8. Comparison of algorithm results

算法	性能指标	y=0.5	y=1	y=2	y=3	y=4	y=5
本文算法	平均时间/s	58.10	153.72	391.88	843.92	1 459.49	1 602.14
	平均结果/h	46.13	48.95	75.17	96.42	120.94	142.55
	平均结果的标准差/h	0.26	0.13	0.24	0.18	0.36	0.58
GA	平均时间/s	83.26	182.45	532.08	1 061.94	1 739.24	1 848.13
	平均结果/h	49.36	52.40	81.58	106.26	137.17	167.72
	平均结果的标准差/h	0.34	0.42	0.31	0.27	0.52	0.70
SA	平均时间/s	73.48	169.02	457.06	975.97	1 647.71	1 779.36
	平均结果/h	51.15	53.62	82.67	105.49	133.47	159.80
	平均结果的标准差/h	0.37	0.27	0.61	0.29	0.46	0.65
RL	平均时间/s	112.83	221.54	603.74	1 231.45	1 987.62	2 110.73
	平均结果/h	50.92	54.17	83.22	107.46	138.81	164.73
	平均结果的标准差/h	0.51	0.48	0.73	0.61	0.79	0.92
DE	平均时间/s	90.75	190.12	545.33	1 092.54	1 780.83	1 892.45
	平均结果/h	48.26	51.38	79.74	104.18	132.92	156.81
	平均结果的标准差/h	0.28	0.30	0.26	0.22	0.39	0.67

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表 9 算法改进率对比

Table 9. Comparison of algorithm improvement rates %

指标类别	比较指标	y=0.5	y=1	y=2	y=3	y=4	y=5
运行时间	本文算法与GA相比改进率	30.22	15.75	26.35	20.53	16.08	13.31
	本文算法与SA相比改进率	20.93	9.05	14.26	13.53	11.42	9.96
	本文算法与RL相比改进率	48.51	30.61	35.09	31.47	26.57	24.10
	本文算法与DE相比改进率	35.98	19.15	28.14	22.76	18.04	15.34
优化结果	本文算法与GA相比改进率	6.54	6.58	7.86	9.26	11.83	15.01
	本文算法与SA相比改进率	9.81	8.71	9.07	8.60	9.39	10.79
	本文算法与RL相比改进率	9.41	9.64	9.67	10.27	12.87	13.46
	本文算法与DE相比改进率	4.41	4.73	5.73	7.45	9.01	9.09
稳定性	本文算法与GA相比改进率	23.53	69.05	22.58	33.33	30.77	17.14
	本文算法与SA相比改进率	29.73	51.85	60.66	37.93	21.74	10.77
	本文算法与RL相比改进率	49.02	72.92	67.12	70.49	54.43	36.96
	本文算法与DE相比改进率	7.14	56.67	7.69	18.18	7.69	13.43

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表 10 不同置信度水平与方差下的模型结果

Table 10. Model result under different confidence levels and variances

置信度水平α	方差σ²	投送时间/h	需求满足比例/%	运行时间/s
0.80	0.10	56.3	86.4	144.5
	0.15	58.5	85.1	144.9
	0.20	59.4	84.4	146.3
0.85	0.10	57.4	86.7	144.1
	0.15	58.2	84.7	143.9
	0.20	58.8	83.9	148.7
0.90	0.10	57.2	86.6	148.2
	0.15	58.4	86.1	145.6
	0.20	59.2	84.9	147.8

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