基于合作协议的集装箱班轮运输船期设计和加油策略联合优化

doi:10.16183/j.cnki.jsjtu.2021.105

摘要/Abstract

摘要：

针对码头运营商与船公司间签署船舶到港多时间窗、多起讫时刻和多装卸速率合作协议下的集装箱班轮运输船期设计和加油策略联合优化问题,考虑各加油港燃油价格差异和折扣因素,建立了以班轮运输服务总成本最小化为目标的船期设计和加油策略非线性混合整数规划模型,结合船舶驾驶操控实际,设计了离散化和线性化模型求解技术方法.以中国远洋海运集团有限公司AEX1航线为例进行了大量模拟验证.算例结果显示,船期设计和加油策略联合优化有助于船公司灵活地调整船舶航速,可显著降低班轮运输服务总成本.敏感性分析表明,随着合作协议中船舶到港时间窗长度的扩大,班轮运输服务总成本和船舶航次加油量都随之降低;无论燃油价格如何变化,船期设计和加油策略联合优化都能有效降低班轮运输服务总成本.

关键词: 班轮运输, 合作协议, 船期设计, 加油策略, 联合优化

Abstract:

This paper studies the joint optimization problem of vessel scheduling and refueling strategy for container liner shipping with cooperative agreements signed by the container terminal operators and the shipping company with multiple vessel arrival time windows, multiple start and end times, and multiple handling rates. A non-linear mixed integer programming model for vessel scheduling and refueling strategy problem of liner shipping is established considering fuel price difference and discount factors at different refueling ports, which aims to minimize the total cost of liner shipping service. A set of discretization and linearization technique is applied to the original model in accordance with the vessel navigation controlling practice. Taking the AEX1 route served by China COSCO Shipping Corporation Limited as an example, a large number of simulation verifications are conducted. The results of numerical examples show that the joint optimization of vessel scheduling and refueling strategy helps shipping companies to flexibly adjust vessel sailing speed. It can significantly reduce the total cost of liner shipping service. The sensitivity analysis indicates that the total cost of liner shipping service and the vessel refueling volume in a voyage will reduce with the expansion of time window of vessel arrival at port in the cooperative agreement. No matter how the fuel price changes, the joint optimization of vessel scheduling and refueling strategy can effectively reduce the total cost of liner shipping service.

Key words: liner shipping, cooperative agreement, vessel scheduling, refueling strategy, joint optimization

中图分类号:

U692.3

李德昌, 杨华龙, 段静茹. 基于合作协议的集装箱班轮运输船期设计和加油策略联合优化[J]. 上海交通大学学报, 2022, 56(7): 953-964.

LI Dechang, YANG Hualong, DUAN Jingru. A Joint Optimization of Vessel Scheduling and Refueling Strategy for Container Liner Shipping with Cooperative Agreements[J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 953-964.

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参考文献 19

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参数	取值
$F D v D 3$	0.013
α	3
C^oc/(美元·周^-1)	300000
W/t	5000
I₀/t	2000
C^F/(美元·次^-1)	1000
$C p i c$ /(美元·TEU^-1·h^-1)	0.5
λ_p₁, λ_p₂/%	95, 90
W_p₁, W_p₂/t	2000, 3000
v^min, v^max/kn	15, 25
m^max/艘	10

港口	D_p/n mile	$C p f 0$ /(美元·t^-1)
青岛	408	429
上海	136	425
宁波	727	438
盐田	1 445	-
新加坡	204	395
巴生	8 012	398
费利克斯托	118	-
鹿特丹	302	372
汉堡	335	288
泽布吕赫	70	-
鹿特丹	8 088	372
巴生	1 649	398
盐田	1 105	-

港口	最早到港时间/h	平均到港时间/h	最迟到港时间/h	最早离港时间/h	平均离港时间/h	最迟离港时间/h	最小加油量/t	平均加油量/t	最大加油量/t
青岛	0	0	0	4	32	80	-	-	-
上海	21	49	98	26	65	109	-	-	-
宁波	31	71	114	41	83	124	-	-	-
盐田	71	113	154	78	124	165	-	-	-
新加坡	137	185	230	142	195	240	-	-	-
巴生	151	204	248	166	214	256	500.00	2058.27	3000.00
费利克斯托	492	549	618	497	571	655	-	-	-
鹿特丹	502	576	660	518	586	674	-	-	-
汉堡	531	598	686	541	611	693	3432.00	4389.05	4500.00
泽布吕赫	556	625	707	565	637	714	-	-	-
鹿特丹	568	640	717	579	651	725	-	-	-
巴生	907	989	1 050	929	1 006	1 060	500.00	589.64	807.58
盐田	995	1 075	1 127	1 124	1 115	1 132	-	-	-
青岛	1 176	1 176	1 176	1 180	1 208	1 256	-	-	-

模型	取值	总加油量/t	营运成本× 10^-6/美元	装卸成本× 10^-6/美元	惩罚成本× 10^-3/美元	燃油成本× 10^-6/美元	库存成本× 10^-6/美元	总成本× 10^-7/美元
[M2]	最小值	5 824	2.100	3.474	0	1.777	3.982	1.2215
	平均值	7 037	2.100	4.689	89	2.265	4.709	1.3852
	最大值	7 647	2.100	6.011	1 399	2.560	5.382	1.5877
[M3]	最小值	6 044	2.100	3.306	0	1.856	4.026	1.2427
	平均值	6 774	2.100	4.896	302	2.169	4.807	1.4274
	最大值	7 624	2.100	6.346	4 426	2.478	5.365	1.8792
[M4]	最小值	5 841	2.100	3.515	0	2.281	3.966	1.2921
	平均值	7 065	2.100	4.682	95	2.759	4.704	1.4340
	最大值	7 651	2.100	6.011	1 528	2.988	5.418	1.6694

时间窗长度/h	各加油港加油量/t			合计/t
时间窗长度/h	巴生(去程)	汉堡	巴生(回程)	合计/t
U[24, 29]	2114.49	4373.21	584.58	7072.28
U[29, 34]	2102.04	4381.56	592.90	7076.50
U[34, 39]	2087.93	4383.51	590.06	7061.50
U[39, 44]	2080.80	4381.93	585.97	7048.70
U[44, 49]	2072.53	4379.49	590.29	7042.31
U[49, 54]	2058.27	4389.05	589.64	7036.96
U[54, 59]	2052.40	4379.20	589.83	7021.43
U[59, 64]	2044.22	4378.04	587.91	7010.17
U[64, 69]	2039.85	4370.51	585.93	6996.29
U[69, 74]	2019.83	4380.21	577.92	6977.96
U[74, 79]	2015.72	4376.71	583.13	6975.56