Article(id=1311360892005085643, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766505600000, receivedDateStr=2025-12-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582400561, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582400561, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582400561, creator=13701087609, updateTime=1790582400561, 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=106, endPage=115, ext={EN=ArticleExt(id=1311360892386767308, articleId=1311360892005085643, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Coordinated optimization of berth allocation and multiple transshipment modes with double-berthing, columnId=1311360870911926761, journalTitle=Navigation of China, columnName=Marine Traffic Engineering, runingTitle=null, highlight=null, articleAbstract=

Transshipment hub ports typically handle high-density water-to-water transshipment, while limited port resources pose significant challenges for efficient berth scheduling. To address this issue, a berth scheduling approach integrating multiple transshipment modes is proposed by incorporating a double-berthing operation, which allows vessels to berth side-by-side at the same berth and conduct direct transshipment via quay cranes. The double-berthing operation is further integrated with indirect transshipment and improved direct transshipment to form a coordinated transshipment framework. A mixed-integer programming model is formulated to jointly determine berth allocation and transshipment mode selection, with the objective of minimizing transshipment costs and vessel-related operational costs. The model incorporates practical constraints including transshipment time windows, vessel matching relationships, and quay crane width limitations. To solve the resulting complex optimization problem, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. Multiple destroy and repair operators together with an adaptive weight updating mechanism are designed to enhance the exploration capability and computational efficiency of the algorithm. Computational experiments of different scales are conducted, and the proposed algorithm is compared with the Gurobi solver, Genetic Algorithm (GA), and Simulated Annealing (SA). In addition, weight sensitivity analysis is performed to evaluate the robustness of the model under different cost weight settings. The applicability of the proposed approach is further verified using real arrival data from feeder berths at Shanghai Yangshan Port. The results show that, compared with traditional scheduling schemes, the proposed approach significantly reduces vessel waiting time and overall completion time while substantially decreasing truck transportation and yard storage costs. Specifically, the total vessel waiting time is reduced by 63.48%, the total operational time by 5.73%, and the overall operational cost by 65.09%. Furthermore, the improved ALNS achieves solutions within 3% of those obtained by Gurobi for small-and medium-sized instances, and obtains high-quality solutions within shorter computation times than GA and SA for large-scale instances, demonstrating strong robustness and computational efficiency.

, authors=Chengji Liang^{1, *}, Yiming Liao¹, Yu Wang¹, Rui Zhao¹, Fengfeng Gu², Lu Han³, authorsList=Chengji Liang, Yiming Liao, Yu Wang, Rui Zhao, Fengfeng Gu, Lu Han, authorCompany=null, correspAuthors=Chengji Liang, 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=1311360892890083789, articleId=1311360892005085643, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于双档靠泊的泊位与多转运模式协同调度优化, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

转运枢纽港通常需面对高密度的水水转运，但港口资源有限，难以制定高效泊位调度方案。针对该问题考虑多船层叠式停靠同一泊位通过岸桥直接转运的双档靠泊方式，并将其与间接转运和改进的直接转运综合，提出一种结合多种转运方式的泊位调度方案。该方案考虑转运时间窗、船舶匹配关系和岸桥宽度等约束，同时决策泊位分配与转运方式选择，以转运成本和船舶相关成本最小化为目标建立混合整数规划模型；针对模型求解复杂性，采用自适应大邻域搜索（ALNS）算法，设计破坏与修复算子及自适应权重更新机制，在不同规模算例下与Gurobi、遗传算法（GA）、模拟退火算法进行对比。通过权重敏感性分析验证模型在不同成本权重设定下的稳定性，并以上海洋山港支线泊位实际到港数据为案例验证方案适用性。结果表明：相比于传统方案，该方案能有效降低船舶等待时间进而减少任务总完工时间，并大幅减少卡车运输和堆场存储成本，船舶总等待时间减少63.48%，总运营时间减少5.73%，总运营成本降低65.09%。改进ALNS在中小规模算例结果与Gurobi求解结果偏差在3%以内，大规模实例相比于GA、SA能够在较短时间内获得高质量解，展现出良好的稳定性与求解效率。

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