影响因子：6.6
所属单位：船舶工程学院
发表刊物：Applied Soft Computing
刊物所在地：荷兰
项目来源：Natural Science Foundation of Heilongjiang Province of China (ZD2022E002); Key Research and Developm
关键字：Cargo and scientific research port; Berth-quay crane-experiment allocation; Two-dimensional packing problem; Genetic algorithm; Neighborhood search
摘要：Against the background of booming global shipping trade and growing demand for marine scientific research, multifunctional ports with both cargo transportation and scientific research functions, namely CSR_Ports, are facing significant operational scheduling challenges. Due to the intricate mutual influence and resource competition among cargo vessels, research vessels, and marine experiments, the existing method struggles to cope with the diversified demands of port operations. To solve the above challenges, this study considers the differences in infrastructure requirements between cargo vessels and research vessels, designs differentiated work areas, abstracts the berth-quay crane-experiment allocation problem into a two-dimensional packing problem with time window constraints, and establishes a dual-objective berth-quay crane-experiment allocation (BQCEA) model to minimize the vessels' average turnaround time and the average variation of the experiment start time. Then, given the limitations of genetic algorithms, such as insufficient adaptive ability and a tendency to fall into local optimality, an improved algorithm, namely NQRGA, is proposed based on neighborhood search, quantum multi-point crossover, and retention mechanism. Finally, the BQCEA model is combined with the NQRGA to propose a novel solution method for solving the berth-quay crane-experiment allocation problem, referred to as BQCEA_NQRGA. The effectiveness of the BQCEA_NQRGA method is evaluated using real operational data from two CSR_Ports in China. The test results show that, compared with the FCFS method, the BQCEA_NQRGA method achieves at least a 49.79 % performance improvement, and the optimization effect gradually increases to 101 % with the increase in the scheduling scale. Compared with the six advanced algorithms selected in this paper, the proposed NQRGA performs best in all six different instances. Notably, the BQCEA_NQRGA method can generate high-quality allocation schemes for 70 vessels and 14 experiments simultaneously within 100 s.
备注：Natural Science Foundation of Heilongjiang Province of China (ZD2022E002); Key Research and Development Program of Heilongjiang Province (GA23A905); Key Research and Development Program of Hainan Province (ZDYF2023GXJS017); National Natural Science Foundation of China (52371315); and Natural Science Foundation of Hainan Province (525MS110).
合写作者：李向阳,耿敬,杨忠仪
第一作者：李明伟
论文类型：期刊论文
通讯作者：洪维强
论文编号：113740
学科门类：工学
文献类型：J
卷号：184
ISSN号：1568-4946
是否译文：否
发表时间：2025-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S1568494625010531?via%3Dihub