Adaptive open-pit mining planning under geological uncertainty

Margaret Armstrong, Tomas Lagos, Xavier Emery, Tito Homem-de-Mello, Guido Lagos, Denis Sauré

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This research project developed an adaptive stochastic optimisation approach for multi-period production scheduling in open-pit mines under geological uncertainty, and compared it to an existing two-stage optimisation method. This new rolling-horizon optimisation approach updates the geological model each time period as new information becomes available. Numerical tests carried out earlier on open-pits of different sizes showed that, on average, the rolling-horizon adaptive policy gave better results than the non-adaptive two-stage approach. The metric used was the percentage gap between the results for each policy and those that would be obtained if the true block grades were perfectly known. This paper extends this earlier work in two ways: firstly, by introducing a second metric — the dollar-value difference between the NPV generated with perfect knowledge of the orebody and those given by the other two optimisation methods. The rolling-horizon approach is better on average than the two-stage approach, but not for all of the geostatistical simulations used to model the geological uncertainty. The second innovation in this paper is to analyse when the new rolling-horizon approach outperforms the non-adaptive one. This depends on the drill-hole spacing. For widely spaced grids, the rolling-horizon approach statistically outperforms the two-stage approach at the 95% confidence level. For very close spacings, both approaches converge toward the results for perfect knowledge.

Original languageEnglish
Article number102086
JournalResources Policy
StatePublished - Aug 2021


  • Adaptive algorithms
  • Geostatistical simulations
  • Learning
  • Stochastic optimisation


Dive into the research topics of 'Adaptive open-pit mining planning under geological uncertainty'. Together they form a unique fingerprint.

Cite this