影响因子：8.0
DOI码：10.1016/j.engappai.2026.115190
所属单位：哈尔滨工程大学船舶工程学院·
发表刊物：Engineering Applications of Artificial Intelligence
刊物所在地：英国
项目来源：National Natural Science Foundation of China (No. 52371315); Key Research and Development Program of
关键字：Cargo and research ports; Research vessels; Marine experiments; Uncertainty; Robust optimization; Differential evolution
摘要：Cargo and Research Ports (CRPs) involve a highly coupled scheduling problem that coordinates cargo vessels, research vessels, and marine experiments, while uncertainties in quay crane efficiency and marine experiment duration further increase operational complexity. Existing deterministic approaches are not suitable for handling such coupled uncertainty in CRP scheduling. To address this issue, this study proposes a robust optimization model, termed ROCREA, based on box uncertainty sets to jointly optimize vessel turnaround time, quay crane movement distance, and marine experiment completion time under uncertainty. A Multi-distribution Adaptive Hybrid Differential Evolution (MAHDE) algorithm is further developed to solve this model. By integrating multi-distribution parameter generation, success-history-based adaptation, and dynamic dual-strategy selection, MAHDE improves search diversity, adaptability, and solution stability for large-scale robust scheduling problems. Computational experiments based on synthetic test instances reflecting the practical operating conditions of CRPs in southern China show that ROCREA consistently maintains a 0% failure rate and outperforms the deterministic model under different uncertainty and congestion conditions. Meanwhile, compared with the benchmark algorithms considered in this study, MAHDE is competitive on small-scale instances and becomes significantly superior on medium-scale and large-scale scheduling problems, while still generating high-quality schedules for large-scale instances (70 vessels and 14 experiments) within 12 min. These results indicate that the proposed framework provides effective decision support for CRP scheduling under uncertainty.
备注：National Natural Science Foundation of China (No. 52371315); Key Research and Development Program of Hainan Province (ZDYF2023GXJS017); Hainan Provincial Natural Science Foundation (525MS110).
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：115190
学科门类：工学
文献类型：J
卷号：179
ISSN号：0952-1976
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/S0952197626014740