Article(id=1263557422027760513, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1738944000000, receivedDateStr=2025-02-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185165173, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185165173, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185165173, creator=13701087609, updateTime=1779185165173, updator=13701087609, issue=Issue{id=1263557421033767765, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='1', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1779185164936, creator=13701087609, updateTime=1779185226134, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263557677813252309, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263557677813252310, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=89, endPage=95, ext={EN=ArticleExt(id=1263557422438802307, articleId=1263557422027760513, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Research on the joint optimization of the scheduling of two automated stacking cranes and the decision-making of flexible buffer blocks in the container yard, columnId=1240655021705065434, journalTitle=Navigation of China, columnName=Port and Waterway Engineering, runingTitle=null, highlight=null, articleAbstract=

In the automated container terminal yard with a vertical layout, the collaborative scheduling and resource allocation of non-crossing Automated Stacking Cranes (ASC) are crucial for improving operation efficiency. The traditional fixed buffer block strategy is prone to causing path conflicts and resource competition bottlenecks due to the uneven distribution of tasks, resulting in limited operation efficiency. To address this issue, this paper proposes a joint optimization framework for the scheduling of two ASCs and the decision-making of flexible buffer blocks. The aim is to achieve efficient collaboration of yard resources through a dynamic task-block matching mechanism and a safety time interval constraint model. Firstly, a mixed-integer programming model is constructed. With the goal of minimizing the task completion time, it couples the path planning of ASCs, task sequences, and the dynamic allocation of buffer blocks. Secondly, a multi-chromosome encoding genetic algorithm is designed, and the sorting crossover and Gaussian mutation strategies are adopted to enhance the solution efficiency for large-scale instances. Numerical experiments show that compared with the single fixed buffer block strategy, the flexible buffer block mechanism can reduce the average task completion time by 13.9%, verifying the adaptability and accuracy of the dynamic allocation. This study provides theoretical support and decision-making basis for the resource allocation in automated terminal yards.

, correspAuthors=Zhihua HU, 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, authorCompany=null, fund=null, authors=null, authorsList=Yanling HUANG, Zhihua HU, Lianggang ZHANG, Xiuliang SUN, Zhilu ZHANG), CN=ArticleExt(id=1263557422774346630, articleId=1263557422027760513, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=集装箱堆场双自动化堆垛机调度与灵活缓冲贝位决策联合优化研究, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

在垂直布局的自动化集装箱码头堆场中，双自动堆垛机（ASC）的协同调度与资源分配是提升作业效率的关键。传统固定缓冲贝位策略因任务分布不均易引发路径冲突与资源竞争瓶颈，导致作业效率受限。为此，本文提出一种双ASC调度与灵活缓冲贝位决策的联合优化框架，旨在通过动态任务-贝位匹配机制与安全时间间隔约束模型，实现堆场资源的高效协同。首先，构建混合整数规划模型，以最小化任务完成时间为目标，耦合ASC路径规划、任务序列与缓冲贝位动态分配；其次，设计多染色体编码遗传算法，通过排序交叉与高斯变异策略增强大规模算例的求解效率。数值试验表明：相较于单一固定缓冲贝位策略，灵活缓冲贝位机制可减少13.9%的平均任务完成时间，验证了动态分配的自适应性与精准性。本研究为自动化码头堆场资源配置提供了理论支持与决策依据。

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黄艳玲（1997—），女，博士研究生，研究方向为自动化集装箱码头运作优化。

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黄艳玲（1997—），女，博士研究生，研究方向为自动化集装箱码头运作优化。

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