Article(id=1282029971271037090, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.07.1289, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740758400000, receivedDateStr=2025-03-01, revisedDate=1746806400000, revisedDateStr=2025-05-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589364115, onlineDateStr=2026-07-09, pubDate=1753632000000, pubDateStr=2025-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589364115, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589364115, creator=13701087609, updateTime=1783589364115, updator=13701087609, issue=Issue{id=1282029960869167928, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='7', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1753632000000', pubDateStr='2025-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589361636, creator='13701087609', updateTime=1783589710614, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031424651903975, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031424656098280, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=31, endPage=39, ext={EN=ArticleExt(id=1282029971472363683, articleId=1282029971271037090, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Multi-objective ambulance scheduling model and algorithm considering psychological deprivation of flood victims, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To reduce casualties and economic losses caused by disasters, a multi-objective ambulance dispatch optimization model that minimizes the maximum rescue time, the standard deviation of ambulance service times, and the total psychological deprivation cost of casualties was developed, while accounting for psychological deprivation factors. Given the non-deterministic polynomial (NP)-hard nature of model, the INSGA-III was proposed, incorporating a multi-chromosome hierarchical encoding strategy and dynamic crossover and mutation methods. Using the 2019 flood in Xingguo County, Ganzhou City, Jiangxi Province as a case study, the proposed INSGA-III was compared with NSGA-III and NSGA-II. Sensitivity analyses on ambulance quantity and relative deprivation cost coefficients demonstrated the effectiveness of model and algorithm. The results show that the maximum rescue time for casualties is minimized to 9.234 hours, the standard deviation of the latest ambulance service times is reduced to 13.156 minutes, and the total psychological deprivation cost of casualties is minimized to 1729.001. With the relative psychological deprivation cost coefficient set at 0.3 and 500 ambulances deployed, the timeliness and fairness of the rescue operations are significantly improved.

, authors=Qi WU¹, Yong LIU^{**, 1, 2}, Liang MA^{1, 2}, Jiawei WU¹, authorsList=Qi WU, Yong LIU, Liang MA, Jiawei WU, authorCompany=null, correspAuthors=Yong LIU, 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=1282029971946320038, articleId=1282029971271037090, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=考虑洪灾伤员心理剥夺的救护车多目标调度模型与算法, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为减轻灾害导致的人员伤亡和经济损失，最小化伤员救助最大时间、救护车最迟服务时间标准差和伤员心理总剥夺成本，综合考虑伤员心理剥夺因素，构建多目标救护车应急救援调度优化模型，结合模型非确定性多项式（NP）难的特性，设计改进的第三代非支配排序遗传算法（INSGA-Ⅲ），采用多染色体分层编码策略及动态交叉变异方法，以2019年江西省赣州市兴国县洪灾为例，对比INSGA-Ⅲ与第三代非支配排序遗传算法（NSGA-Ⅲ）、第二代非支配排序遗传算法（NSGA-Ⅱ），开展救护车数量和相对剥夺成本系数的灵敏度分析，并验证模型和算法的有效性。结果表明：最小化伤员救助的最大时间为9.234 h，最小化救护车的最迟服务时间标准差为13.156 min，最小化伤员心理总剥夺成本为1 729.001。伤员的心理相对剥夺成本系数控制在0.3，配置500辆救护车，能有效提高救援的时效性和公平性。

, authors=吴琪¹, 刘勇副教授^{**, 1, 2}, 马良教授^{1, 2}, 武嘉伟¹, authorsList=吴琪, 刘勇副教授, 马良教授, 武嘉伟, authorCompany=null, correspAuthors=刘勇副教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=zir81fKtKxB9NibcxXyWqg==, magXml=k1Ff99WUd2hbz+WYRAA3Og==, pdfUrl=null, pdf=ngPnndAkzR9tFdTQq3WNKg==, pdfFileSize=5270853, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=GloxZB/ytvbhbjgsDi1L9A==, mapNumber=null, fund=null)}, authors=[Author(id=1282029972332196014, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282029971271037090, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wuqiusst@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282029972407693488, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282029971271037090, authorId=1282029972332196014, language=EN, stringName=Qi WU, firstName=Qi, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282029972512551089, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282029971271037090, authorId=1282029972332196014, language=CN, stringName=吴琪, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹上海理工大学 管理学院，上海 200093, bio={"content":"

吴琪（2001—），女，福建莆田人，硕士研究生，主要研究方向为灾后应急调度和智能优化。Email: wuqiusst@163.com。

"}, bioImg=null, bioContent=

吴琪（2001—），女，福建莆田人，硕士研究生，主要研究方向为灾后应急调度和智能优化。Email: wuqiusst@163.com。

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