影响因子：8.5
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Swarm and Evolutionary Computation
刊物所在地：荷兰
关键字：Cargo and research ports; Berth and quay crane allocation; Experiment allocation; Particle swarm optimization; Q-learning
摘要：Multifunctional ports integrating cargo and research operations (CRPs) face unprecedented scheduling complexities due to spatiotemporal conflicts among cargo vessels, research vessels, and marine experiments. To resolve the aforementioned resource conflicts, this study proposes a hierarchical spatiotemporal coordination framework that establishes differentiated operational zones and experiment time windows. Then, a multi-objective joint scheduling model (BCAEA) is formulated to integrate berth allocation, quay crane assignment, and experiment arrangement, simultaneously minimizing shipowners' and operational costs while maximizing experimental efficiency. To solve this large-scale optimization problem, an enhanced particle swarm optimization algorithm (QLEPSO) is developed, incorporating a position update strategy pool, Q-learning-based strategy selection, and adaptive parameter control. Numerical experiments using real operational data from Chinese CRPs demonstrate that QLEPSO outperforms standard PSO by 47.17% in solution quality for large-scale problems. Moreover, the proposed BCAEA_QLEPSO method generates high-quality allocation schemes for instances involving 90 vessels and 18 experiments within 1 minute, validating the effectiveness of integrating reinforcement learning with swarm intelligence for complex port scheduling.
合写作者：李明伟,洪维强
第一作者：李向阳
论文类型：期刊论文
通讯作者：杨忠仪
论文编号：102315
学科门类：工学
文献类型：J
卷号：102
ISSN号：2210-6502
是否译文：否
发表时间：2026
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2210650226000350?via%3Dihub