集装箱码头泊位-岸桥减排协同调度优化研究

doi:10.19678/j.issn.1000-3428.0069599

摘要/Abstract

摘要：

随着“双碳”目标的持续推进，港口产业进一步升级。在考虑港区船舶废气排放的前提下，建立船舶服务成本和排放成本最小化的双目标泊位-岸桥协同调度优化模型，并设计基于非支配排序遗传算法Ⅱ (NSGA-Ⅱ)的改进算法，即基于强化学习-Q学习NSGA-Ⅱ (RL-Q-NSGA-Ⅱ)。通过对赤湾集装箱码头进行实证分析，将双目标减排协同调度优化模型分别采用改进算法、原始NSGA-Ⅱ算法与先到先服务调度模式得到的结果进行定量对比，实验结果表明，RL-Q-NSGA-Ⅱ算法在迭代速度、收敛性及帕累托前沿解聚集程度方面表现更优，与原始NSGA-Ⅱ算法相比，船舶服务成本和港区船舶大气污染排放成本分别优化12.19%和6.04%，总成本优化8.39%, 与先到先服务模式相比，船舶服务成本和港区船舶大气污染排放成本分别优化18.68%和3.79%，总成本优化9.82%；同时，港区船舶废气排放成本与服务成本呈负相关关系，若码头仅考虑船舶服务效率或码头作业成本，都将导致港区废气排放的社会成本大幅增加。该模型和算法可为港方和船公司在不同情形下做出合理的泊位岸桥调度计划提供参考。

关键词: 水路运输, 多目标优化, 非支配排序遗传算法Ⅱ, 泊位岸桥联合调度, 强化学习-Q学习

Abstract:

In this study, a dual-objective berth-bridge cooperative scheduling optimization model is proposed to minimize ship service cost and emission cost, and an improved algorithm, namely Reinforcement Learning based Q-learning NSGA-Ⅱ (RL-Q-NSGA-Ⅱ), based on Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) is designed. Through an empirical analysis of the Chiwan container terminal, the results obtained using the improved algorithm, the original NSGA-Ⅱ algorithm, and the first-come first-served scheduling model are quantitatively compared. The results show that the RL-Q-NSGA-Ⅱ algorithm performs better in terms of the iteration speed, convergence, and Pareto front deaggregation degree. Compared with the original NSGA-Ⅱ algorithm, the ship service cost and port ship air pollution emission cost are optimized by 12.19% and 6.04%, respectively, and the total cost is optimized by 8.39%. Compared with the first-come first-served model, the service cost and emission cost are optimized by 18.68% and 3.79%, respectively, and the total cost is optimized by 9.82%. In addition, a negative correlation is observed between ship exhaust emission cost and service cost. If the port considers only the ship service efficiency or the dock operation cost, the social cost of port exhaust emission will increase significantly. The results demonstrate that the proposed model and algorithm can provide a reference for port and shipping companies to make reasonable berth and quay crane scheduling plans for different situations.

Key words: water transportation, multi-objective optimization, Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ), combined berth-shore and bridge scheduling, reinforcement learning-Q learning

杨嘉卉, 尤再进, 倪立夫, 赵煜, 李婉莹. 集装箱码头泊位-岸桥减排协同调度优化研究[J]. 计算机工程, 2025, 51(10): 381-391.

YANG Jiahui, YOU Zaijin, NI Lifu, ZHAO Yu, LI Wanying. Study on Optimization of Berth-Quay Crane Emission Reduction Cooperative Scheduling in Container Terminals[J]. Computer Engineering, 2025, 51(10): 381-391.

https://www.ecice06.com/CN/Y2025/V51/I10/381

图/表 18

图1 集装箱码头泊位-岸桥调度流程

Fig.1 Procedure of container terminal berth-quay bridge scheduling

图2 泊位调度方案

Fig.2 Berth scheduling scheme

图3 RL-Q-NSGA-Ⅱ算法流程

Fig.3 RL-Q-NSGA-Ⅱ algorithm procedure

图4 RL-Q-NSGA-Ⅱ算法求解结果

Fig.4 Solution results RL-Q-NSGA-Ⅱ algorithm

图5 船舶泊位-岸桥分配时空图

Fig.5 Space-time diagram of ship berth-quay bridge allocation

图6 不同模型的成本对比柱状图

Fig.6 Bar chart of cost comparison under different models

图7 船舶服务成本变化

Fig.7 Changes of ship service cost

图8 船舶排放成本变化

Fig.8 Changes of ship emission cost

图9 NSGA-Ⅱ、RL-Q-NSGA-Ⅱ算法Pareto前沿解分布

Fig.9 NSGA-Ⅱ, RL-Q-NSGA-Ⅱ algorithm Pareto frontier solution distribution

参考文献 25

1	IMO. Fourth GHG study[EB/OL]. [2024-02-10]. https://www.imo.org/en/OurWork/Environment/Pages/Fourth-IMO-Greenhouse-Gas-Study-2020.aspx.
2	交通运输部. 2021 《绿色交通"十四五"发展规划》[EB/OL]. [2024-02-10]. https://www.gov.cn/zhengce/zhengceku/2022-01/21/content_5669662.
	Ministry of Transport. 2021 "14th five-year plan for green transportation"[EB/OL]. [2024-02-10]. https://www.gov.cn/zhengce/zhengceku/2022-01/21/content_5669662. (in Chinese)
3	IMAI A, NISHIMURA E, PAPADIMITRIOU S. The dynamic berth allocation problem for a container port. Transportation Research Part B: Methodological, 2001, 35(4): 401- 417. doi: 10.1016/S0191-2615(99)00057-0
4	PARK Y M, KIM K H. A scheduling method for berth and quay cranes. OR Spectrum, 2003, 25(1): 1- 23. doi: 10.1007/s00291-002-0109-z
5	BIERWIRTH C, MEISEL F. A survey of berth allocation and quay crane scheduling problems in container terminals. European Journal of Operational Research, 2010, 202(3): 615- 627. doi: 10.1016/j.ejor.2009.05.031
6	IRIS Ç, LALLA-RUIZ E, LAM J S L, et al. Mathematical programming formulations for the strategic berth template problem. Computers & Industrial Engineering, 2018, 124, 167- 179.
7	郑红星, 张敬涛, 刘保利. 考虑潮汐影响的连续泊位和岸桥集成调度. 计算机集成制造系统, 2018, 24(10): 2599- 2611.
	ZHENG H X, ZHANG J T, LIU B L. Integrated continuous berth allocation and quay crane scheduling under tidal influence at container terminal. Computer Integrated Manufacturing Systems, 2018, 24(10): 2599- 2611.
8	杨劼, 高红, 刘巍. 离散泊位布局下的泊位岸桥动态协调调度. 计算机工程与应用, 2018, 54(3): 265- 270.
	YANG J, GAO H, LIU W. Integrated dynamic berth and quay-crane scheduling based on discrete berth layout. Computer Engineering and Applications, 2018, 54(3): 265- 270.
9	NG S K W, LOH C, LIN C B, et al. Policy change driven by an AIS-assisted marine emission inventory in Hong Kong and the Pearl River Delta. Atmospheric Environment, 2013, 76, 102- 112. doi: 10.1016/j.atmosenv.2012.07.070
10	SONG S. Ship emissions inventory, social cost and eco-efficiency in Shanghai Yangshan port. Atmospheric Environment, 2014, 82, 288- 297. doi: 10.1016/j.atmosenv.2013.10.006
11	NGUYEN P N, WOO S H, KIM H. Ship emissions in hotelling phase and loading/unloading in Southeast Asia ports. Transportation Research Part D: Transport and Environment, 2022, 105, 103223. doi: 10.1016/j.trd.2022.103223
12	CHEN S K, MENG Q, JIA P, et al. An operational-mode-based method for estimating ship emissions in port waters. Transportation Research Part D: Transport and Environment, 2021, 101, 103080. doi: 10.1016/j.trd.2021.103080
13	GOLIAS M, BOILE M, THEOFANIS S, et al. The berth-scheduling problem. Journal of the Transportation Research Board, 2010, 2166(1): 20- 27. doi: 10.3141/2166-03
14	HU Q M, HU Z H, DU Y Q. Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 2014, 70, 1- 10.
15	HE J L. Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving. Advanced Engineering Informatics, 2016, 30(3): 390- 405. doi: 10.1016/j.aei.2016.04.006
16	WANG T S, LI M, HU H T. Berth allocation and quay crane-yard truck assignment considering carbon emissions in port area. International Journal of Shipping and Transport Logistics, 2019, 11(2/3): 216. doi: 10.1504/IJSTL.2019.099275
17	KENAN N, JEBALI A, DIABAT A. The integrated quay crane assignment and scheduling problems with carbon emissions considerations. Computers & Industrial Engineering, 2022, 165, 107734.
18	王丹, 李丹阳, 赵利昕, 等. 考虑多种污染气体排放的集装箱码头泊位-岸桥-集卡集成调度优化. 工业工程与管理, 2023, 28(1): 131- 143.
	WANG D, LI D Y, ZHAO L X, et al. Optimization of container terminal's integrated scheduling of berths, quay crane and internal trucks considering pollutant emissions. Industrial Engineering and Management, 2023, 28(1): 131- 143.
19	王军, 郭力铭, 杜剑, 等. 基于动态学习的泊位调度方案优化. 交通运输系统工程与信息, 2018, 18(5): 197- 203.
	WANG J, GUO L M, DU J, et al. Berth scheduling scheme optimization based on dynamic learning. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(5): 197- 203.
20	DAS NEVES MILISSE NZUALO T, DE OLIVEIRA C E F, PÉREZ T O A, et al. Ship speed optimisation in green approach to tidal ports. Applied Ocean Research, 2021, 115, 102845. doi: 10.1016/j.apor.2021.102845
21	YANG X, TENG F, WANG G H. Incorporating environmental co-benefits into climate policies: a regional study of the cement industry in China. Applied Energy, 2013, 112, 1446- 1453. doi: 10.1016/j.apenergy.2013.03.040
22	REN Q, LUO F, DING W C, et al. An improved NSGAⅡ algorithm based on site-directed mutagenesis method for multi-objective optimization[C]//Proceedings of IEEE Symposium Series on Computational Intelligence. Washington D. C., USA: IEEE Press, 2019: 176-181.
23	张国富, 管燕妮, 苏兆品, 等. 多阶段应急物资多目标连续分配问题建模与求解. 计算机工程, 2024, 50(12): 329- 345. doi: 10.19678/j.issn.1000-3428.0068634
	ZHANG G F, GUAN Y N, SU Z P, et al. Modeling and solution of multi-stage emergency materials multi-objective continuous distribution problem. Computer Engineering, 2024, 50(12): 329- 345. doi: 10.19678/j.issn.1000-3428.0068634
24	THE PORT OF LOS ANGELES. San pedro bay ports emissions inventory methodology report[EB/OL]. [2024-02-10]. https://kentico.portoflosangeles.org/getmedia/ad5ec383-8dc6-4652-ae0d-81b6ea4c7819/SPBP_Emissions_Inventory_Methodology_v3a.
25	生态环境部. 2019年度减排项目中国区域电网基准线排放因子[EB/OL]. [2024-02-10]. https://www.mee.gov.cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386.
	Ministry of Ecology and Environment. 2019 annual emission reduction project China regional power grid baseline emission factors[EB/OL]. [2024-02-10]. https://www.mee.gov.cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386.

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