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Author (up) Armstrong, M.; Valencia, J.; Lagos, G.; Emery, X. doi  openurl
  Title Constructing Branching Trees of Geostatistical Simulations Type
  Year 2022 Publication Mathematical Geosciences Abbreviated Journal Math. Geosci.  
  Volume Early Access Issue Pages  
  Keywords Mine planning; Multi-stage programming with recourse; Scenario reduction; Geological uncertainty; Adaptive optimisation  
  Abstract This paper proposes the use of multi-stage stochastic programming with recourse for optimised strategic open-pit mine planning. The key innovations are, firstly, that a branching tree of geostatistical simulations is developed to take account of uncertainty in ore grades, and secondly, scenario reduction techniques are applied to keep the trees to a manageable size. Our example shows that different mine plans would be optimal for the downside case when the deposit turns out to be of lower grade than expected compared to when it is of higher grade than expected. Our approach further provides th  
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  ISSN 1874-8961 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000725900700001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1506  
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Author (up) Lagos, T.; Armstrong, M.; Homem-de-Mello, T.; Lagos, G.; Saure, D. doi  openurl
  Title A framework for adaptive open-pit mining planning under geological uncertainty Type
  Year 2021 Publication Optimization And Engineering Abbreviated Journal Optim. Eng.  
  Volume 72 Issue Pages 102086  
  Keywords Mine planning; Geostatistics; Stochastic optimization; Adaptive algorithms; Iterative learning algorithm  
  Abstract Mine planning optimization aims at maximizing the profit obtained from extracting valuable ore. Beyond its theoretical complexity-the open-pit mining problem with capacity constraints reduces to a knapsack problem with precedence constraints, which is NP-hard-practical instances of the problem usually involve a large to very large number of decision variables, typically of the order of millions for large mines. Additionally, any comprehensive approach to mine planning ought to consider the underlying geostatistical uncertainty as only limited information obtained from drill hole samples of the mineral is initially available. In this regard, as blocks are extracted sequentially, information about the ore grades of blocks yet to be extracted changes based on the blocks that have already been mined. Thus, the problem lies in the class of multi-period large scale stochastic optimization problems with decision-dependent information uncertainty. Such problems are exceedingly hard to solve, so approximations are required. This paper presents an adaptive optimization scheme for multi-period production scheduling in open-pit mining under geological uncertainty that allows us to solve practical instances of the problem. Our approach is based on a rolling-horizon adaptive optimization framework that learns from new information that becomes available as blocks are mined. By considering the evolution of geostatistical uncertainty, the proposed optimization framework produces an operational policy that reduces the risk of the production schedule. Our numerical tests with mines of moderate sizes show that our rolling horizon adaptive policy gives consistently better results than a non-adaptive stochastic optimization formulation, for a range of realistic problem instances.  
  Address [Lagos, Tomas; Saure, Denis] Univ Chile, Dept Ind Engn, Santiago, Chile, Email: tito.hmello@uai.cl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1389-4420 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000569001700001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1244  
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Author (up) Reus, L.; Pagnoncelli, B.; Armstrong, M. doi  openurl
  Title Better management of production incidents in mining using multistage stochastic optimization Type
  Year 2019 Publication Resources Policy Abbreviated Journal Resour. Policy  
  Volume 63 Issue Pages 13 pp  
  Keywords Mining incidents; Optimal policy; Stochastic dual dynamic programming; Risk-aversion; CVaR; Julia language  
  Abstract Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine planning model that takes into account these types of incidents, as well as random prices. When confronted by production difficulties, mines which have contracts to supply customers have a range of flexibility options including buying on the spot market, or taking material from a stockpile if they have one. Earlier work on this subject was limited in that the optimization could only be carried out for a few stages (up to 5 years) and in that it only analyzed the risk-neutral case. By using decomposition schemes, we are now able to solve large-scale versions of the model efficiently, with a horizon of up to 15 years. We consider decision trees with up to 615 scenarios and implement risk aversion using Conditional Value-at-Risk, thereby detecting its effect on the optimal policy. The results provide a “roadmap” for mine management as to optimal decisions, taking future possibilities into account. We present extensive numerical results using the new sddp.jl library, written in the Julia language, and discuss policy implications of our findings.  
  Address [Reus, Lorenzo] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: lorenzo.reus@uai.cl;  
  Corporate Author Thesis  
  Publisher Elsevier Sci Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0301-4207 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000488888100004 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1165  
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