Article(id=1321893303465922980, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755878400000, receivedDateStr=2025-08-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093523135, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093523135, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093523135, creator=13701087609, updateTime=1793093523135, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=200, endPage=208, ext={EN=ArticleExt(id=1321893303658860965, articleId=1321893303465922980, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Scheduling Optimization of Underground Metal Mines Based on Monte Carlo Simulation, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Production scheduling of underground metal mines is confronted with severe challenges caused by multi-source uncertainties, such as geological conditions, equipment status, and safety risks, while traditional deterministic scheduling models struggle to guarantee system stability and operational efficiency. To address this issue, a Monte Carlo simulation-based scheduling optimization model(MC-MOO) for underground metal mines was proposed. Firstly, probability distribution models were established for multi-source uncertainty factors. Subsequently, Monte Carlo scenario simulation was integrated with the NSGA-Ⅱ multi-objective optimization algorithm to develop a closed-loop framework of "probabilistic modeling-scenario simulation-multi-objective optimization", enabling the comprehensive optimization of scheduling cost, production stability, and equipment load balancing. The case study results show that compared with the traditional deterministic scheduling models, the total scheduling cost of the MC-MOO model is reduced by 5.97%, the average monthly output is increased by 16.53%, the standard deviation of output is reduced by 41.82%, and the equipment load balance is increased by 23.61%. It provides an effective modeling and solving paradigm for mine intelligent scheduling under uncertain environment.

, authors=Zhaoyang Ma^{1, 2, 3}, Yuansheng Zhang^{1, 2, 3}, authorsList=Zhaoyang Ma, Yuansheng Zhang, 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=1321893303969239462, articleId=1321893303465922980, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于蒙特卡洛模拟的地下金属矿山调度优化研究, 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=

地下金属矿山生产调度面临地质条件、设备状态及安全风险等多源不确定性的严峻挑战，传统确定性调度模型难以保障系统的稳定性与运行效率。为此，提出一种基于蒙特卡洛模拟的地下金属矿山调度优化模型（MC-MOO）。首先，构建多源不确定性因素的概率分布模型；继而，融合蒙特卡洛场景模拟与NSGA-Ⅱ多目标优化算法，建立“概率建模-场景模拟-多目标优化”的闭环求解框架，以综合优化调度成本、产量稳定性及设备负荷均衡度。案例研究结果表明，相较于传统确定性调度模型，MC-MOO模型的总调度成本降低了5.97%，月均产量提高了16.53%，产量标准差降低了41.82%，设备负荷均衡度提高了23.61%，为不确定性环境下矿山智能调度提供了行之有效的建模与求解范式。

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马朝阳（1992—），女，河北唐山人，博士，高级工程师，主要从事矿业经济分析研究。E-mail:machaoyang@bgrimm.com

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马朝阳（1992—），女，河北唐山人，博士，高级工程师，主要从事矿业经济分析研究。E-mail:machaoyang@bgrimm.com

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