Study on Optimization of Berth-Quay Crane Emission Reduction Cooperative Scheduling in Container Terminals

doi:10.19678/j.issn.1000-3428.0069599

Abstract

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

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

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.

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.

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Figures/Tables 18

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

Fig.2 Berth scheduling scheme

Fig.3 RL-Q-NSGA-Ⅱ algorithm procedure

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

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

Fig.6 Bar chart of cost comparison under different models

Fig.7 Changes of ship service cost

Fig.8 Changes of ship emission cost

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

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