Article(id=1240655511868207858, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.02.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1713283200000, receivedDateStr=2024-04-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724924294, onlineDateStr=2026-03-17, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724924294, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724924294, creator=13701087609, updateTime=1773724924294, updator=13701087609, issue=Issue{id=1240655508231746242, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='2', pageStart='1', pageEnd='180', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724923427, creator=13701087609, updateTime=1773725151164, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656463488667953, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656463492862258, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655508231746242, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=52, endPage=61, ext={EN=ArticleExt(id=1240655512098894590, articleId=1240655511868207858, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=An optimization study of pilot scheduling and pilot training based on a two-stage heuristic algorithm, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=
Optimising pilot scheduling can reduce working hours and labour costs for pilots, while offering new approaches to pilot training. This paper addresses the scheduling of pilots for inbound and outbound vessels in a one-way waterway by establishing a joint optimisation model, with the aim of minimising total vessel waiting time, total pilot working hours and labour costs. It proposes a two-stage heuristic algorithm that combines an imperialist competitive algorithm, an adaptive evolutionary strategy, and a variable neighbourhood search (VNS) algorithm. Case studies and comparative experiments demonstrate the effectiveness of the model and algorithm. Compared with the traditional first-come, first-served (FCFS) rule, the proposed approach reduces pilots' average working hours and labour costs by 17.4% and 12.4%, respectively. Therefore, integrating the model and algorithm into pilot training programmes or operational guidelines could enhance coordination among port departments, improve organisational efficiency and service quality, and support the development of green and smart ports.
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通过研究引航员排班调度优化问题,不仅能减少引航员工作时间和人力成本,而且可为引航员的教学训练提供新的方法。针对引航员排班问题,以船舶在港总等待时间、引航员总工作时间和引航员人力成本最小作为调度优化目标,建立基于单向航道的进出港船舶与引航员联合调度优化模型。结合模型设计基于革命分裂算子自适应进化策略的多目标帝国竞争算法(ICA)和变邻域算法的两阶段启发式算法进行求解。试验结果表明:该模型及算法合理有效,相较于传统的先到先服务(FCFS)调度规则,引航员的平均工作时间和人力成本分别减少了17.4%和12.4%。所提出的模型和算法可引入引航员的培训内容或教学指南,以便在实际工作中更好地整合港口多个部门的调度工作,提高港口的组织效率和服务水平,推进绿色、智慧港口建设。
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1.Education Department, Liaoning Normal University, Dalian 116081, China
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1. 辽宁师范大学 教育学部,辽宁 大连 116081
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刘磊(1989—),男,讲师,博士研究生,研究方向为航海教育。E-mail:dmuliulei@dlmu.edu.cn
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刘磊(1989—),男,讲师,博士研究生,研究方向为航海教育。E-mail:dmuliulei@dlmu.edu.cn
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