Article(id=1311360859922850202, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.010, 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=1790582392911, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582392911, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582392911, creator=13701087609, updateTime=1790582392911, 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=88, endPage=97, ext={EN=ArticleExt(id=1311360860178702747, articleId=1311360859922850202, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Research on optimization of multi-port area heterogeneous tugboat scheduling considering carbon emissions, columnId=1240655020656489358, journalTitle=Navigation of China, columnName=Green Shipping, runingTitle=null, highlight=null, articleAbstract=

With the green transformation of port equipment, tugboat scheduling faces new opportunities and challenges. On the one hand, the deployment of new energy tugboats helps reduce port operating costs and carbon emissions; on the other hand, research on new energy tugboats remains relatively scarce. To address this research gap, a mixed-integer linear programming model for heterogeneous tugboat scheduling under the context of multiple port areas is established based on the characteristics of hybrid tugboats, aiming to minimize port costs, tugboat operation costs, and carbon emission costs. To solve the model, a Multi-Verse Optimization algorithm embedded with heuristic rules is applied, and its effectiveness and accuracy are verified through comparison with the Gurobi solver. In large-scale instances, the proposed algorithm achieved objective values that were, on average, 4% and 3.25% better than those obtained by the GA and IGA, respectively. The research results show that configuring high-power tugboats can effectively reduce ship port costs and tugboat operation costs. However, when the number of high-power tugboats is too large, it may increase operation costs, and at this point, appropriately configuring lower-power tugboats can leverage their low operation cost advantage. An appropriate configuration of a certain number of hybrid tugboats can effectively reduce the total scheduling cost as well as the carbon emission cost of tugboat operations. Meanwhile, the proportion of carbon emission costs in the overall operational cost can be continuously reduced from 8% to approximately 1.51%.

, authors=Liangyong Chu^{1, 2, *}, Jiachen Lin¹, Yiming Zhang¹, authorsList=Liangyong Chu, Jiachen Lin, Yiming Zhang, authorCompany=null, correspAuthors=Liangyong Chu, 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=1311360862217134492, articleId=1311360859922850202, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=考虑碳排放的多港区异构拖轮调度优化研究, columnId=1240655020820067227, journalTitle=中国航海, columnName=绿色航运, runingTitle=null, highlight=null, articleAbstract=

随着港口设备的绿色转型，拖轮调度问题面临着新的机遇与挑战。一方面新能源拖轮的投用有利于降低港口作业成本与碳排放量，另一方面当前针对新能源拖轮的研究较为匮乏。针对这一研究局限，根据混合动力拖轮的特征，以船舶在港成本、拖轮作业成本及碳排放成本最小化为目标构建了多港区背景下的异构拖轮调度混合整数线性规划模型。针对模型的特点，采用嵌入特定启发式规则的多元宇宙优化（MVO）算法进行求解，在小规模算例上通过与Gurobi对比验证了算法的有效性与准确性，在大规模算例求解中平均可求出优于遗传算法（GA）与改进遗传算法（IGA）4%与3.25%的目标值。研究结果显示：配置大功率的拖轮可有效降低船舶在港成本与拖轮作业成本，但大功率拖轮数量过多时会相对增加拖轮的作业成本，此时适当配置较小功率的拖轮可以发挥出其作业成本低的优势；有效配置一定数量的混合动力拖轮可以降低总调度成本及拖轮碳排放成本，同时拖轮碳排放成本所占作业成本的比例可以从8%不断降低至约1.51%。

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