Article(id=1311360888930660779, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.013, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762358400000, receivedDateStr=2025-11-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582399827, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582399827, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582399827, creator=13701087609, updateTime=1790582399827, 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=116, endPage=125, ext={EN=ArticleExt(id=1311360889144570284, articleId=1311360888930660779, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Optimization of port collection and distribution network considering demand uncertainty and inland port congestion, columnId=1311360870911926761, journalTitle=Navigation of China, columnName=Marine Traffic Engineering, runingTitle=null, highlight=null, articleAbstract=

Demand uncertainty and inland port congestion pose significant challenges to the efficient operation of port collection and distribution systems. This paper proposes a bi-level programming model that simultaneously account for demand uncertainty and inland port congestion. The upper-level model optimizes inland port location and capacity configuration by minimizing construction costs and congestion costs, while the lower-level model optimizes transport paths by minimizing transportation costs, transfer costs, carbon emission costs, and time costs. A Genetic Algorithm is employed to solve the model, with empirical analysis based on data from the Liaoning port cluster and its northeastern hinterland. The results show that higher-grade inland ports effectively absorb demand fluctuations and reduce congestion costs, while reducing carbon emissions by increasing the share of railway transport. A maximum regret value of θ=0.25 achieves the optimal balance between total cost and stability of the Liaoning network. Additionally, a carbon tax of ￥0.5/kg achieves the best emission reduction and congestion control outcomes.

, authors=Dan Wang^*, Songyue Yu, Yuan Zhao, authorsList=Dan Wang, Songyue Yu, Yuan Zhao, authorCompany=null, correspAuthors=Dan Wang, 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=1311360889417200045, articleId=1311360888930660779, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=考虑需求不确定和内陆港拥堵的港口集疏运网络优化, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

需求不确定性与内陆港拥堵问题，对港口集疏运系统的高效运转带来严峻挑战。文章构建了同时考量需求不确定性和内陆港拥堵的港口集疏运网络双层规划模型：上层以内陆港建设成本与拥堵成本最小化为目标，优化内陆港的选址与容量配置；下层以运输成本、转运成本、碳排放成本及时间成本最小为目标，优化运输路径规划。采用遗传算法对模型求解，并基于辽宁港口群及东北腹地数据开展实证分析。结果表明：高等级内陆港可有效吸纳需求扰动、降低拥堵成本，且能通过提升铁路运输占比推动碳排放削减；当鲁棒优化最大遗憾值θ=0.25时，可实现辽宁港口集疏运网络总成本与稳定性的最优平衡；当碳税设定为0.5元/kg时，能够同时达成最佳的减排效果与拥堵控制效果。

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