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Aiming at the problems of poor scheduling scheme, low utilization rate and high cost of unmanned trucks in large open-pit coal mines, an optimization method based on digital twin simulation was proposed, and a digital twin system framework for unmanned trucks was constructed. The digital twin simulation model was built by AnyLogic software to realize the real-time mapping of physical and virtual space. A mathematical model aiming at minimizing transportation cost, minimizing total transportation time and maximizing truck utilization was established, and a Quantum Enhanced Particle Swarm Optimization (QEPSO) algorithm was designed to solve the model. The verification results of an open-pit coal mine in Xinjiang show that compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm and the genetic algorithm, the maximum completion time of the QEPSO algorithm is reduced by 5.84%-11.34%, the total transportation cost is reduced by 5.12%-10.49%, and the truck utilization rate is increased by 4.32%-9.14%, with a faster convergence speed and better stability. The digital twin scheduling scheme shortens the task completion time by 73 min compared with the traditional scheme, effectively improves the transportation efficiency. The study provides a feasible technical path and method support for the intelligent scheduling of unmanned trucks in open-pit coal mines.

, authors=Xiongwei Ruan¹, Yiping Yuan¹, Lijie Yu^{1, 2}, authorsList=Xiongwei Ruan, Yiping Yuan, Lijie Yu, authorCompany=null, correspAuthors=Yiping Yuan, 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=1321893303625302719, articleId=1321893302169879229, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于数字孪生仿真的大型露天煤矿无人卡车调度优化研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对大型露天煤矿无人卡车调度方案差、利用率低以及成本高等难题，提出了基于数字孪生仿真的优化方法，构建了无人卡车数字孪生系统框架。利用AnyLogic软件搭建数字孪生仿真模型，实现物理与虚拟空间的实时映射。建立以运输成本最小化、总运输时间最短化及卡车利用率最大化为目标的数学模型，并设计量子增强粒子群优化算法（Quantum Enhanced Particle Swarm Optinization, QEPSO）进行求解。新疆某露天煤矿实例验证结果表明：相较于传统粒子群算法、改进粒子群算法和遗传算法，QEPSO算法的最大完工时间降低了5.84%~11.34%、总运输成本降低了5.12%~10.49%、卡车利用率提升了4.32%~9.14%，且其收敛速度更快、稳定性更好；数字孪生调度方案较传统方案任务完成时间缩短了73 min，有效提高了运输效率，为露天煤矿无人卡车的智能化调度提供了可行的技术路径和方法支持。

, authors=阮雄威¹, 袁逸萍¹, 於立杰^{1, 2}, authorsList=阮雄威, 袁逸萍, 於立杰, authorCompany=null, correspAuthors=袁逸萍, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=CN0++NDXMntVdTkLYuWfTQ==, magXml=DBW3RFN8fxpnBNzr7Qv1jw==, pdfUrl=null, pdf=eZNIzZYznnX6tfRdgouYSQ==, pdfFileSize=4596097, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=58jP9p8HmmPslyNZW7Jhjw==, mapNumber=null, fund=null)}, authors=[Author(id=1321893304032150215, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893302169879229, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=18074087935@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893304116036297, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893302169879229, authorId=1321893304032150215, language=EN, stringName=Xiongwei Ruan, firstName=Xiongwei, middleName=null, lastName=Ruan, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi, Xinjiang 830017, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893304191533770, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893302169879229, authorId=1321893304032150215, language=CN, stringName=阮雄威, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．新疆大学 智能制造现代产业学院，新疆 乌鲁木齐　830017, bio={"content":"

阮雄威（1998—），男，湖北咸宁人，硕士研究生，主要研究方向为露天煤矿无人卡车调度优化。E-mail:18074087935@163.com

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阮雄威（1998—），男，湖北咸宁人，硕士研究生，主要研究方向为露天煤矿无人卡车调度优化。E-mail:18074087935@163.com

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