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Author (up) Canessa, G.; Moreno, E.; Pagnoncelli, B.K. doi  openurl
  Title The risk-averse ultimate pit problem Type
  Year 2021 Publication Optimization And Engineering Abbreviated Journal Optim. Eng.  
  Volume 22 Issue Pages 2655-2678  
  Keywords Ultimate pit; Mining; Risk-averse optimization; Integer programming  
  Abstract In this work, we consider a risk-averse ultimate pit problem where the grade of the mineral is uncertain. We derive conditions under which we can generate a set of nested pits by varying the risk level instead of using revenue factors. We propose two properties that we believe are desirable for the problem: risk nestedness, which means the pits generated for different risk aversion levels should be contained in one another, and additive consistency, which states that preferences in terms of order of extraction should not change if independent sectors of the mine are added as precedences. We show that only an entropic risk measure satisfies these properties and propose a two-stage stochastic programming formulation of the problem, including an efficient approximation scheme to solve it. We illustrate our approach in a small self-constructed example, and apply our approximation scheme to a real-world section of the Andina mine, in Chile.  
  Address [Canessa, Gianpiero] Univ Adolfo Ibanez, PhD Program Ind Engn & Operat Res, Diagonal Las Torres 2640, Santiago 7941169, Chile, Email: canessa@kth.se  
  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:000557140000001 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1219  
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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) Ogunmodede, O.; Lamas, P.; Brickey, A.; Bogin, G.; Newman, A. doi  openurl
  Title Underground production scheduling with ventilation and refrigeration considerations Type
  Year 2022 Publication Optimization And Engineering Abbreviated Journal Optim. Eng.  
  Volume Early Access Issue Pages  
  Keywords Underground mine scheduling; Integer programming applications; Resource-constrained project scheduling; Ventilation; Diesel equipment; Refrigeration  
  Abstract Underground mine production scheduling determines when, if ever, activities associated with the extraction of ore should be executed. The accumulation of heat in the mine where operators are working is a major concern. At the time of this writing, production scheduling and ventilation decisions are not made in concert. Correspondingly, heat limitations are largely ignored. Our mixed-integer program maximizes net present value subject to constraints on precedence, and mill and extraction capacities with the consideration of heat using thermodynamic principles, while affording the option of activating refrigeration to mitigate heat accumulation. In seconds to hours, depending on the problem size (up to thousands of activities and 900 daily time periods), a corresponding methodology that exploits the mathematical problem structure provides schedules that maintain a safe working environment for mine operators; optimality gaps are no more than 15% and average less than half that for otherwise-intractable instances.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language 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:000741919400001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1519  
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