Article(id=1263533686977323503, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.02.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779179506295, onlineDateStr=2026-05-19, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779179506295, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779179506295, creator=13701087609, updateTime=1779179506295, updator=13701087609, issue=Issue{id=1263533678655881533, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='2', pageStart='1', pageEnd='163', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1779179504312, creator=13701087609, updateTime=1779179563167, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263533925629047616, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263533925629047617, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=153, endPage=163, ext={EN=ArticleExt(id=1263533688076231167, articleId=1263533686977323503, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Ship deployment and scheduling considering carbon intensity management and sulfur emission limits, columnId=1240655020656489358, journalTitle=Navigation of China, columnName=Green Shipping, runingTitle=null, highlight=null, articleAbstract=

Under the Carbon Intensity Indicator (CⅡ) rules of the International Maritime Organization (IMO), most theoretical studies manage ship carbon intensity primarily by reducing carbon emissions. However, reducing carbon emissions at the expense of ship transport work no longer aligns with the goal of carbon peaking intensity. Therefore, considering sulfur emission limits, a model was developed to determine whether fuel switching or scrubber retrofitting should be adopted. Combining with carbon intensity management, a decision model for the ship deployment and scheduling problem is proposed, subject to the constraints on sailing speed, fleet deployment, and carbon intensity compliance. To solve the proposed mixed-integer nonlinear programming model, a hybrid algorithm combining linearization and CPLEX is designed. The model is validated using five routes operated by COSCO Shipping. The results show that, compared with the genetic algorithm, the proposed hybrid algorithm increases the solution time slightly by 7.6%, while reducing the operating cost significantly by 33.4%, and all solutions satisfy the engineering constraints. Without carbon intensity management, the carbon intensity of some routes deteriorates to a non-compliant level, which confirms that carbon intensity management can effectively reduce the risk of ship downgrade and service suspension. Based on the above results, two managerial insights are obtained. First, to reduce fleet fuel consumption, liner companies should reduce ship deadweight while still meeting cargo demand, and lower sailing speed within the allowable range. To reduce fleet carbon intensity, besides lowering speed within the allowable range, liner companies should also increase cargo demand to increase ship deadweight. Second, a higher reduction factor imposes stricter carbon intensity requirement. Limited by the minimum and maximum sailing speeds, carbon intensity management requires the deployment of ships with larger deadweight. To avoid carbon intensity non-compliance and excessively low ship loading rate, liner companies should focus on improving transport work by increasing cargo demand.

, correspAuthors=Ran 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, authorCompany=null, fund=null, authors=null, authorsList=Jian Du, Yizhuo Ren, Yixuan Chen, Ran Zhang, Mingyue Yang, Xinran Wen), CN=ArticleExt(id=1263533690030776849, articleId=1263533686977323503, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=考虑碳强度管理与硫排放限制的船舶配置与调度研究, columnId=1240655020820067227, journalTitle=中国航海, columnName=绿色航运, runingTitle=null, highlight=null, articleAbstract=

国际海事组织（IMO）的船舶碳强度（CⅡ）规则下，理论研究大多仅依靠减少碳排放量来管理船舶碳强度。然而，以牺牲船舶运输功为代价减少碳排放量，不再符合碳强度达峰目标。故考虑硫排放限制，建模开展燃油更换或脱硫塔改装的措施选择。结合碳强度管理，以航速范围、配船数量、碳强度达标为约束，提出船舶配置与调度问题的决策模型。针对所提出的混合整数非线性规划模型，设计了线性化与CPLEX的混合算法。以中远海运5条航线进行验证，结果表明：与遗传算法相比，本文混合算法求解时间略增7.6%，但运营成本降低33.4%，且方案满足所有工程约束；无碳强度管理时，部分航线碳强度恶化至不合规，证实碳强度管理可有效规避船舶降级停航风险。基于上述结果得到管理启示：1）为降低船队油耗，班轮公司在满足货运需求前提下应减少船舶载重吨，并在调节范围内降低航速。为降低船队碳强度，除在调节范围内降低航速外，还应承揽更多货运需求以提升船舶载重吨。2）折减系数增大会提高碳强度要求，而受最小最大航速限制，碳强度管理需配置更大容量船舶。为避免碳强度不达标与船舶满载率过低，班轮公司应聚焦借助货运需求增加来提高运输功。

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杜剑（1991—），男，副教授，博士，研究方向为交通运输规划与管理。

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杜剑（1991—），男，副教授，博士，研究方向为交通运输规划与管理。

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