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A Framework for Adaptive Open-pit Mining Planning under Geological Uncertainty

Tomas Lagos(tomas.lagos.gonzalez***at***gmail.com)
Margaret Armstrong(margaret.armstrong171***at***gmail.com)
Tito Homem-de-Mello(tito.hmello***at***uai.cl)
Guido Lagos(guido.lagos.barrios***at***gmail.com)
Denis Saure(dsaure***at***gmail.com)

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 100,000 for a small mine, to 10,000,000 for a large one. 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 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 the non-adaptive stochastic program for a range of realistic problem instances.

Keywords: Mine planning, geostatistics , stochastic optimization, adaptive algorithms, learning

Category 1: Applications -- OR and Management Sciences (Scheduling )

Category 2: Stochastic Programming

Category 3: Other Topics (Dynamic Programming )


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Entry Submitted: 04/15/2020
Entry Accepted: 04/15/2020
Entry Last Modified: 04/15/2020

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