Article(id=1322559806498517755, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, 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=1740240000000, receivedDateStr=2025-02-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252429841, onlineDateStr=2026-10-29, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252429841, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252429841, creator=13701087609, updateTime=1793252429841, updator=13701087609, issue=Issue{id=1322559792544072145, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='1', pageStart='1', pageEnd='306', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252426515, creator='13701087609', updateTime=1793252675339, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322560836246286849, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322560836246286850, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=52, endPage=59, ext={EN=ArticleExt(id=1322559806653707004, articleId=1322559806498517755, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Improvement of Dynamic Programming Method Based on Ant Colony Optimization Algorithm and Efficient Mining and Stripping Plan for Large-Scale Open-Pit Mines, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To improve the efficiency and accuracy of the mining and stripping plan for large-scale open-pit mines, a dynamic sorting of mining increment bodies(ACO-DP) integrating the ant colony optimization (ACO) algorithm and dynamic programming (DP) method was proposed. Combined with the distributed search mechanism of ACO, the state transition path selection strategy was reconstructed, which significantly reduced the computational complexity. Through its practical application in an open-pit quarry in Guangdong, the significant effects of the ACO-DP method in improving the efficiency of mining and stripping plan, optimizing resource allocation and enhancing economic benefits were verified. The results show that the ACO-DP method shortens the planning time of the quarry from 3 000 minutes to 6.45 minutes, with an efficiency improvement of 99.8%. The stripping ratio is optimized from 0.39 to 0.124, with a decrease of 68.2%, and the net present value gradually increases from 244.99 million yuan to a peak of 253.93 million yuan. The ACO-DP method can be integrated into a mine digital twin system, supporting real-time dynamic adjustment and multi-objective collaborative optimization, and providing a new idea for intelligent mine planning under the "double carbon" goal.

, authors=Yuqi LYU¹, Xiaodong WANG¹, Xuelin HUANG², Hong XIANG², Yingchao TENG², authorsList=Yuqi LYU, Xiaodong WANG, Xuelin HUANG, Hong XIANG, Yingchao TENG, authorCompany=null, correspAuthors=Xiaodong WANG, 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=1322559807068943101, articleId=1322559806498517755, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于蚁群优化算法的动态规划改进及大型露天矿山高效采剥规划, columnId=1226462294490657775, journalTitle=矿业研究与开发, columnName=采矿与矿山充填, runingTitle=null, highlight=null, articleAbstract=

为提高大型露天矿山采剥规划的效率与精度，提出了一种融合蚁群优化（ACO）算法与动态规划（DP）的开采增量体动态排序方法（ACO-DP）。结合ACO的分布式搜索机制，重构状态转移路径选择策略，显著降低计算复杂度。通过广东某露天采石场的实际应用，验证ACO-DP方法在提升采剥规划效率、优化资源配置和提高经济效益方面的显著作用。结果表明，ACO-DP方法将采石场的规划时间从3 000 min缩短至6.45 min，效率提升了99.8%，采剥比从0.39优化至0.124，降幅为68.2%，净现值从24 499万元逐步增加到峰值25 393万元。ACO-DP方法可集成至矿山数字孪生系统，支持实时动态调整与多目标协同优化，为“双碳”目标下的矿山智能规划提供了新思路。

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吕玉琪（1999—），男，山东济宁人，硕士，主要从事矿山数字化方面的研究。E-mail:lvyuqi2022@163.com

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吕玉琪（1999—），男，山东济宁人，硕士，主要从事矿山数字化方面的研究。E-mail:lvyuqi2022@163.com

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