Article(id=1311360864813408702, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1768406400000, receivedDateStr=2026-01-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582394078, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582394078, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582394078, creator=13701087609, updateTime=1790582394078, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=47, endPage=56, ext={EN=ArticleExt(id=1311360866403049919, articleId=1311360864813408702, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Two-stage hybrid cuckoo search algorithm for solving the dual-resource berth allocation problem, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

To address the challenge of coordinating and scheduling construction vessels in large-scale water projects such as waterway regulation, this problem is modeled for the first time as a Dual-Resource Berth Allocation Problem (DR-BAP). A mathematical model is established with the objective of minimizing vessel waiting costs and resource preference mismatch costs. To solve this Non-deterministic Polynomial-time hard (NP-hard) problem, a Two-Stage Hybrid Cuckoo Search Algorithm (TSH-CSA) is proposed. In the first stage, the algorithm employs dynamic priorities, a load balancing guidance strategy, and a historical congestion feedback penalty mechanism for macroscopic resource allocation. It integrates domain knowledge and feedback information to optimize fitness evaluation. In the second stage, the algorithm integrates an adaptive dual-population mechanism and an intelligent restart mechanism to achieve a microscopic, refined solution. Overall, the algorithm establishes a global iterative framework of "allocation-scheduling-feedback" to enhance its global optimization capability. Simulation experiments based on a waterway regulation project show that, compared with the standard Cuckoo Search Algorithm, TSH-CSA exhibits significant improvements in solution quality, convergence speed, and robustness. These results verify the effectiveness and advancement of the proposed model and algorithm in solving complex engineering scheduling problems.

, authors=Dan Zhang^{1, *}, Qianqian Li¹, Jinlu Sheng¹, Zhenbin Peng², authorsList=Dan Zhang, Qianqian Li, Jinlu Sheng, Zhenbin Peng, authorCompany=null, correspAuthors=Dan Zhang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311360866738594240, articleId=1311360864813408702, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=两阶段混合布谷鸟算法求解双资源泊位分配, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

针对航道整治等大型水上工程中施工船舶协同调度难题，首次将其建模为双资源泊位分配问题（DR-BAP），建立以最小化船舶等待成本和资源偏好不匹配成本为目标的数学模型。为求解此非确定性多项式（NP-hard）问题，提出两阶段混合布谷鸟搜索算法（TSH-CSA）：第一阶段采用动态优先级、负载均衡引导策略及历史拥堵反馈惩罚机制进行宏观资源分配，融合领域知识与反馈信息优化适应度评估；第二阶段融合自适应双种群与智能重启机制实现微观精细化求解，算法整体构建“分配—调度—反馈”全局迭代框架以提升全局寻优能力。以某航道整治工程为背景的仿真实验表明，相较于标准布谷鸟搜索算法，TSH-CSA在求解质量、收敛速度与鲁棒性上均显著提升，验证了模型与算法解决复杂工程调度问题的有效性与先进性。

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