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Author Lagos, T.; Armstrong, M.; Homem-de-Mello, T.; Lagos, G.; Saure, D.
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 (up)
Call Number UAI @ alexi.delcanto @ Serial 1244
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