Article(id=1281683192159638463, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.05.1436, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736784000000, receivedDateStr=2025-01-14, revisedDate=1742400000000, revisedDateStr=2025-03-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783506685533, onlineDateStr=2026-07-08, pubDate=1748361600000, pubDateStr=2025-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783506685533, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783506685533, creator=13701087609, updateTime=1783506685533, updator=13701087609, issue=Issue{id=1281683189131359212, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='5', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1748361600000', pubDateStr='2025-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783506684811, creator='13701087609', updateTime=1783589333681, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029843684504545, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029843684504546, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=204, endPage=212, ext={EN=ArticleExt(id=1281683192369353664, articleId=1281683192159638463, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Dynamic path planning for truck-assault boat collaborative transportation in flood disaster scenario, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

In order to solve the multi-period and multi-batch relief delivery problem under flood disaster scenarios, a routing model was constructed for collaborative relief delivery of trucks and assault boats by comprehensively considering factors such as the dynamic changes of disaster situations, and the collaborative transportation of trucks and assault boats. The objective of the model was to minimize the total distribution time within each period. A HHA, which combines the improved ant colony algorithm(IACA) with a genetic algorithm(GA), was designed for solving this problem. Compared with the ant colony algorithm and the simulated annealing algorithm, the experimental results show that the HHA provides truck-assault boat collaborative distribution routing solutions under the dynamically changing disaster situations in a relatively short time. The HHA effectively reduces the total distribution time and improves the efficiency of relief delivery. The rationality, feasibility, and effectiveness of the HHA were demonstrated. The number of flooded areas and the number of assault boats have a significant impact on the routing of relief delivery. Emergency logistics decision-making departments should weigh the disaster situation and resource allocation of each flooded area, and dispatch a reasonable number of assault boats to ensure the efficiency of relief delivery.

, authors=Changshi LIU¹, Tao LIU², Guanghong LIU³, Peng SUN^{**, 1}, Junyu LI², Rui QIAO², authorsList=Changshi LIU, Tao LIU, Guanghong LIU, Peng SUN, Junyu LI, Rui QIAO, authorCompany=null, correspAuthors=Peng SUN, 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=1281683192843310019, articleId=1281683192159638463, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=洪涝灾害情景下卡车-冲锋艇协同运输的动态路径规划, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为解决洪涝灾害情景下应急物资多周期多批次配送问题，综合考虑洪涝灾情动态变化、卡车-冲锋艇协同运输等因素，以各周期内应急物资的总配送时间最短为目标构建卡车-冲锋艇协同运输的路径规划模型，并设计改进蚁群算法（IACA）结合遗传算法（GA）的混合启发式算法（HHA）求解；采用多类型算例开展试验，结果表明：HHA能根据动态变化的灾情快速计算出合理的卡车-冲锋艇协同运输方案，有效缩短应急物资的总配送时间，提高配送效率；水淹区数量与冲锋艇数量对应急物资配送路径规划有显著影响，应急物流部门应根据各水淹区受灾情况和资源配置实际，派遣合理数量的冲锋艇，保障应急物流效率。

, authors=刘长石教授¹, 刘涛², 刘光洪³, 孙鹏副教授^{**, 1}, 李君宇², 乔睿², authorsList=刘长石教授, 刘涛, 刘光洪, 孙鹏副教授, 李君宇, 乔睿, authorCompany=null, correspAuthors=孙鹏副教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=kRiuX8DH+YyMNRaq+hQpWg==, magXml=dM87X/JPl9FVOVdbmXesiQ==, pdfUrl=null, pdf=E+4FBwMC8O1nIpuczY8SYA==, pdfFileSize=2551466, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=yyCJDXKAejzzJRyslxek8Q==, mapNumber=null, fund=null)}, authors=[Author(id=1281683193375986638, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683192159638463, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=liuchangshi964@126.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281683193464067024, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683192159638463, authorId=1281683193375986638, language=EN, stringName=Changshi LIU, firstName=Changshi, middleName=null, lastName=LIU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Business Administration, Hunan University of Technology and Business, Changsha Hunan 410205, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281683193539564497, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683192159638463, authorId=1281683193375986638, language=CN, stringName=刘长石教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹湖南工商大学 工商管理学院，湖南 长沙 410205, bio={"content":"

刘长石（1975—），男，湖南邵阳人，博士，教授，主要从事物流与供应链管理、数字经济等方面的研究。E-mail: liuchangshi964@126.com。

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刘长石（1975—），男，湖南邵阳人，博士，教授，主要从事物流与供应链管理、数字经济等方面的研究。E-mail: liuchangshi964@126.com。

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