Article(id=1282036049505398982, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282036038860255373, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.11.1572, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750003200000, receivedDateStr=2025-06-16, revisedDate=1758384000000, revisedDateStr=2025-09-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1783590813279, onlineDateStr=2026-07-09, pubDate=1764259200000, pubDateStr=2025-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783590813279, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783590813279, creator=13701087609, updateTime=1783590813279, updator=13701087609, issue=Issue{id=1282036038860255373, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='11', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1764259200000', pubDateStr='2025-11-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783590810742, creator='13701087609', updateTime=1783593801313, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048582291276616, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282036038860255373, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048582295470921, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282036038860255373, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=56, endPage=64, ext={EN=ArticleExt(id=1282036049929023687, articleId=1282036049505398982, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Optimization strategy of snow removal resources allocation before airport disaster, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To improve the efficiency of pre-disaster airport snow removal resource allocation, collaborative operations between equipment and artificial snow removal were considered. Snow removal resilience was introduced to represent snow removal efficiency, and a multi-objective optimization analysis model of airport pre-disaster snow removal resource reserve was established. The NSGA-Ⅱ was used to solve the optimal solution set and Pareto frontier. The C-OWA operator was introduced to calculate the objective weights of snow removal resilience and cost, and composite weight was determined by combining the subjective weight. Based on the TOPSIS method, the best scheme of pre-disaster snow removal resource reserve was obtained, and the influence of the subjective weight assigned to snow removal resilience on the decision results was analyzed. The results show that the maximum and minimum resilience of the optimal solution set of the multi-objective optimization model considering resilience and cost are 108 600 and 93 928 m³/h, respectively, and the costs are 1.118 million yuan and 798 600 yuan, respectively. The snow removal resilience of the best solution in the optimal solution set of pre-disaster snow removal resource reserve is 97 200 m³/h, and the snow removal cost is 920 700 yuan. Compared with the optimal solution set, the resilience of the optimal solution obtained by optimization analysis is increased by 31.5%, indicating that the established pre-disaster snow removal resource allocation method is feasible. Snow removal resilience is positively correlated with the subjective weight. For example, when the subjective weight is 0, 0.2 and 0.4, the corresponding snow removal resilience are 85 496, 89 600 and 97 200 m³/h, respectively.

, authors=Xin HUANG¹, Ru LI¹, Ping XU^{1, 2}, Kun WU¹, authorsList=Xin HUANG, Ru LI, Ping XU, Kun WU, authorCompany=null, correspAuthors=null, 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=1282036050663026888, articleId=1282036049505398982, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=机场灾前除雪资源配置优化策略, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为提升灾前机场除雪资源配置效率，考虑机场设备除雪和人工除雪协同作业，引入除雪恢复力考虑除雪效率，建立机场灾前除雪资源配置多目标优化分析模型，采用非支配排序遗传算法Ⅱ（NSGA-Ⅱ）求解得到最优解集和Pareto前沿，引入连续有序加权平均（C-OWA）算子计算除雪恢复力和成本的客观权重，并结合主观权重确定综合权重，基于优劣解距离法（TOPSIS）分析得到灾前除雪资源配置的最佳方案，并分析除雪恢复力的主观权重对决策结果的影响。结果表明：考虑恢复力和成本的多目标优化模型的最优解集的最大和最小恢复力分别为108 600和93 928m³/h，成本分别为111.8万元和79.86万元，灾前除雪资源配置最优解集中最佳方案的除雪恢复力为97 200m³/h，除雪成本为92.07万元，相对最优解集而言，采用优化分析得到的最优方案的恢复力增加了31.5%，说明建立的灾前除雪资源配置方法可行；除雪恢复力与主观权重呈正相关关系，如主观权重为0、0.2和0.4时对应的除雪恢复力分别为85 496、89 600和97 200 m³/h。

, authors=黄信教授¹, 李茹¹, 徐平^{1, 2}, 吴堃副教授¹, authorsList=黄信教授, 李茹, 徐平, 吴堃副教授, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=rp9ZeomVlXMJVRrg0cnqpg==, magXml=4FuEoEMPq2zqS8JBh+2yrA==, pdfUrl=null, pdf=EcsbbeWbY4CVwFk2VUfrTg==, pdfFileSize=4208860, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=FDwAD0in/TqINwpMMHocNg==, mapNumber=null, fund=null)}, authors=[Author(id=1282036052797927633, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282036049505398982, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=x_huang@cauc.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282036053116694739, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282036049505398982, authorId=1282036052797927633, language=EN, stringName=Xin HUANG, firstName=Xin, middleName=null, lastName=HUANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282036053196386517, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282036049505398982, authorId=1282036052797927633, language=CN, stringName=黄信教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹中国民航大学 交通科学与工程学院，天津 300300, bio={"content":"

黄信（1983—），男，安徽六安人，博士，教授，主要从事机场基础设施与工程结构等方面的研究。E-mail: x_huang@cauc.edu.cn。

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黄信（1983—），男，安徽六安人，博士，教授，主要从事机场基础设施与工程结构等方面的研究。E-mail: x_huang@cauc.edu.cn。

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