Impact of Geological Uncertainty at Different Stages of the Open-Pit Mine Production Planning Process

  • Enrique JélvezEmail author
  • Nelson Morales
  • Julián M. Ortíz
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)


This work addresses the long-term open-pit mine production planning. The solution for this problem indicates how and when the ore reserves will be extracted in order to maximize the value of the mining business, generating a promise that commits the mine production over time. Usually, due to the complexity of the problem, the planning process is divided into stages, generating three related problems that are sequentially solved to obtain a tentative production plan, that is: (i) determination of the final pit, which consists of delimiting the subregion of the mine where the extraction will be carried out; (ii) pushbacks selection, that corresponds to a partition of the final pit that allows to guide the sequence of extraction and to control the design; and finally, (iii) temporary production scheduling, which defines when the different zones will be extracted and which of them will be processed.

One of the disadvantages of the traditional methodology is the geological uncertainty is not taken into account, despite the great impact it can have on the production objectives. In this work we show some approaches to incorporate the uncertainty by means of conditional simulations to different stages of production planning, evaluating their impact. The results show that, on one hand, it is possible to increase the expected value of the business, and on the other hand, to reduce the risk of failure to meet production targets, allowing to generate more robust plans. In the case study presented, the results show that it is possible to obtain a discounted expected value increase of 2% and an uncertainty total cost decrease of 69% with respect to the usual methodology, which does not consider the geological uncertainty. Therefore, better decisions could be made in the long-term open pit mine production planning.


Open-pit mine production scheduling Geological uncertainty Stochastic mixed integer programming Conditional simulation 



Enrique Jélvez and Nelson Morales were supported by CONICYT/PIA Project AFB180004 – Advanced Mining Technology Center – Universidad de Chile.


  1. 1.
    Lerchs, H., Grossmann, I.: Optimal design of open-pit mines. Trans. C.I.M. 68, 17–24 (1965)Google Scholar
  2. 2.
    Dimitrakopoulos, R., Farrelly, C., Godoy, M.: Moving forward from traditional optimization: grade uncertainty and risk effects in open-pit design. Min. Technol. 111(1), 82–88 (2002)CrossRefGoogle Scholar
  3. 3.
    Morales, N., Seguel, S., Cáceres, A., Jélvez, E., Alarcón, M.: Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning. Minerals 9(2), 108 (2019)CrossRefGoogle Scholar
  4. 4.
    Emery, X., Lantuéjoul, C.: TBSIM: a computer program for conditional simulation of three-dimensional Gaussian random fields via the turning bands method. Comput. Geosci. 32(10), 1615–1628 (2006)CrossRefGoogle Scholar
  5. 5.
    Ravenscroft, P.: Risk analysis for mine scheduling by conditional simulation. Trans. Inst. Min. Metall. Sect. A. Min. Ind. 101, A104–A108 (1992)Google Scholar
  6. 6.
    Smith, M., Dimitrakopoulos, R.: The influence of deposit uncertainty on mine production scheduling. Int. J. Surf. Min. Reclam. Environ. 13(4), 173–178 (1999)CrossRefGoogle Scholar
  7. 7.
    Osanloo, M., Gholamnejad, J., Karimi, B.: Long-term open pit mine production planning: a review of models and algorithms. Int. J. Min. Reclam. Environ. 22(1), 3–35 (2008)CrossRefGoogle Scholar
  8. 8.
    Jélvez, E.: Metodología multietapa para la planificación de la producción de largo plazo en minas a rajo abierto bajo incertidumbre geológica. Ph.D. thesis, Departamento de Ingeniería de Minas, Universidad de Chile, Santiago, pp. 1–189 (2017)Google Scholar
  9. 9.
    Godoy, M.: The efficient management of geological risk in long-term production scheduling of open pit mines. Ph.D. thesis, University of Queensland, Brisbane, pp. 1–256 (2003)Google Scholar
  10. 10.
    Dimitrakopoulos, R., Ramazan, S.: Stochastic integer programming for optimising long term production schedules of open pit mines: methods, application and value of stochastic solutions. Min. Technol. 117(4), 155–160 (2008)CrossRefGoogle Scholar
  11. 11.
    Mai, N.L., Topal, E., Erten, O., Sommerville, B.: A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming. Resour. Policy 62, 571–579 (2018)CrossRefGoogle Scholar
  12. 12.
    Jélvez, E., Morales, N., Ortíz, J.M.: Stochastic ultimate pit limit: an efficient Frontier analysis under geological uncertainty (submitted)Google Scholar
  13. 13.
    Rockafellar, R., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–42 (2000)CrossRefGoogle Scholar
  14. 14.
    Marcotte, D., Caron, J.: Ultimate open pit stochastic optimization. Comput. Geosci. 51, 238–246 (2013)CrossRefGoogle Scholar
  15. 15.
    Jélvez, E., Morales, N., Askari-Nasab, H.: A new model for automated pushback selection. Comput. Oper. Res. (2018, in press)Google Scholar
  16. 16.
    Meagher, C., Dimitrakopoulos, R., Avis, D.: Optimized open pit mine design, pushbacks and the gap problem: a review. J. Min. Sci. 50(3), 508–526 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Enrique Jélvez
    • 1
    Email author
  • Nelson Morales
    • 1
  • Julián M. Ortíz
    • 2
  1. 1.Delphos Mine Planning Laboratory, Advanced Mining Technology Center and Department of Mining EngineeringUniversidad de ChileSantiagoChile
  2. 2.The Robert M. Buchan Department of MiningQueen’s UniversityKingstonCanada

Personalised recommendations