Joint stochastic optimisation of stope layout, production scheduling and access network

Cristina Penadillo, R. Dimitrakopoulos, Mustafa Kumral
{"title":"Joint stochastic optimisation of stope layout, production scheduling and access network","authors":"Cristina Penadillo, R. Dimitrakopoulos, Mustafa Kumral","doi":"10.1177/25726668241242230","DOIUrl":null,"url":null,"abstract":"The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"50 223","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/25726668241242230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
井口布局、生产调度和通道网络的联合随机优化
分层停采方法的三个主要优化要素是井口布置、生产计划(或井口排序)和通道网络。对这些部分进行联合优化可进一步增加地下采矿项目的价值。由于计算上的困难,文献中还没有考虑到这一潜力,而且问题是按顺序解决的。本文提出了一种新的联合优化模型来整合这些组件。此外,建议的优化模型还纳入了随机模拟,以捕捉与所开采的相关矿藏品位相关的不确定性和可变性。该优化模型以两阶段随机整数编程(SIP)公式为基础,使项目净现值(NPV)最大化,计划稀释度最小化。在一个小型铜矿床中应用所提出的方法,结果表明 SIP 优于混合整数编程的结果。对于七年的矿山寿命而言,SIP 模型产生的净现值要高出 20%,这说明了开发联合优化方案以及考虑品位不确定性和可变性的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A modified approach for cut-off grade and production rate optimization in block caving projects Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex Wet inrush susceptibility assessment at the Deep Ore Zone mine using a random forest machine learning model Development of an intelligent evolution algorithm for open pit mines’ long-term production scheduling using the concept of block aggregation A reinforcement learning approach for selecting infill drilling locations considering long-term production planning in mining complexes with supply uncertainty
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1