IDEAS home Printed from
   My bibliography  Save this article

Better management of production incidents in mining using multistage stochastic optimization


  • Reus, Lorenzo
  • Pagnoncelli, Bernardo
  • Armstrong, Margaret


Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine-planning model that takes into account these types of incidents, as well as random prices. When confronted by production difficulties, mines which have contracts to supply customers have a range of flexibility options including buying on the spot market, or taking material from a stockpile if they have one. Earlier work on this subject was limited in that the optimization could only be carried out for a few stages (up to 5 years) and in that it only analyzed the risk-neutral case. By using decomposition schemes, we are now able to solve large-scale versions of the model efficiently, with a horizon of up to 15 years. We consider decision trees with up to 615 scenarios and implement risk aversion using Conditional Value-at-Risk, thereby detecting its effect on the optimal policy. The results provide a “roadmap” for mine management as to optimal decisions, taking future possibilities into account. We present extensive numerical results using the new sddp.jl library, written in the Julia language, and discuss policy implications of our findings.

Suggested Citation

  • Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  • Handle: RePEc:eee:jrpoli:v:63:y:2019:i:c:43
    DOI: 10.1016/j.resourpol.2019.101404

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Margaret Armstrong & Alain Galli & Rija Razanatsimba, 2012. "Using multistage stochastic optimisation to manage major production incidents," Post-Print hal-00771482, HAL.
    2. Slade, Margaret E., 2001. "Valuing Managerial Flexibility: An Application of Real-Option Theory to Mining Investments," Journal of Environmental Economics and Management, Elsevier, vol. 41(2), pages 193-233, March.
    3. Alonso-Ayuso, Antonio & Carvallo, Felipe & Escudero, Laureano F. & Guignard, Monique & Pi, Jiaxing & Puranmalka, Raghav & Weintraub, Andrés, 2014. "Medium range optimization of copper extraction planning under uncertainty in future copper prices," European Journal of Operational Research, Elsevier, vol. 233(3), pages 711-726.
    4. Bernardo K. Pagnoncelli & Adriana Piazza, 2017. "The optimal harvesting problem under price uncertainty: the risk averse case," Annals of Operations Research, Springer, vol. 258(2), pages 479-502, November.
    5. Margaret Insley & Kimberly Rollins, 2005. "On Solving the Multirotational Timber Harvesting Problem with Stochastic Prices: A Linear Complementarity Formulation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 735-755.
    6. Brennan, Michael J & Schwartz, Eduardo S, 1985. "Evaluating Natural Resource Investments," The Journal of Business, University of Chicago Press, vol. 58(2), pages 135-157, April.
    7. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    8. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
    9. Cortazar, Gonzalo & Kovacevic, Ivo & Schwartz, Eduardo S., 2015. "Expected commodity returns and pricing models," Energy Economics, Elsevier, vol. 49(C), pages 60-71.
    10. B. K. Pagnoncelli & S. Ahmed & A. Shapiro, 2009. "Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications," Journal of Optimization Theory and Applications, Springer, vol. 142(2), pages 399-416, August.
    11. G Barbarosoǧlu & Y Arda, 2004. "A two-stage stochastic programming framework for transportation planning in disaster response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 43-53, January.
    12. James E. Smith & Kevin F. McCardle, 1998. "Valuing Oil Properties: Integrating Option Pricing and Decision Analysis Approaches," Operations Research, INFORMS, vol. 46(2), pages 198-217, April.
    13. Henrik Andersson, 2007. "Are commodity prices mean reverting?," Applied Financial Economics, Taylor & Francis Journals, vol. 17(10), pages 769-783.
    14. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    15. Dehghani, Hesam & Ataee-pour, Majid & Esfahanipour, Akbar, 2014. "Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree," Resources Policy, Elsevier, vol. 39(C), pages 124-133.
    16. Najafi, Amir Abbas & Mushakhian, Siamak, 2015. "Multi-stage stochastic mean–semivariance–CVaR portfolio optimization under transaction costs," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 445-458.
    17. Savolainen, Jyrki, 2016. "Real options in metal mining project valuation: Review of literature," Resources Policy, Elsevier, vol. 50(C), pages 49-65.
    18. Cortazar, Gonzalo & Schwartz, Eduardo S, 1993. "A Compound Option Model of Production and Intermediate Inventories," The Journal of Business, University of Chicago Press, vol. 66(4), pages 517-540, October.
    19. Wan, Yang & Clutter, Michael L. & Mei, Bin & Siry, Jacek P., 2015. "Assessing the role of U.S. timberland assets in a mixed portfolio under the mean-conditional value at risk framework," Forest Policy and Economics, Elsevier, vol. 50(C), pages 118-126.
    20. James E. Smith & Robert F. Nau, 1995. "Valuing Risky Projects: Option Pricing Theory and Decision Analysis," Management Science, INFORMS, vol. 41(5), pages 795-816, May.
    21. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    22. Andy Philpott & Vitor de Matos & Erlon Finardi, 2013. "On Solving Multistage Stochastic Programs with Coherent Risk Measures," Operations Research, INFORMS, vol. 61(4), pages 957-970, August.
    23. Guigues, Vincent & Sagastizábal, Claudia, 2012. "The value of rolling-horizon policies for risk-averse hydro-thermal planning," European Journal of Operational Research, Elsevier, vol. 217(1), pages 129-140.
    24. Webby, R.B. & Adamson, P.T. & Boland, J. & Howlett, P.G. & Metcalfe, A.V. & Piantadosi, J., 2007. "The Mekong—applications of value at risk (VaR) and conditional value at risk (CVaR) simulation to the benefits, costs and consequences of water resources development in a large river basin," Ecological Modelling, Elsevier, vol. 201(1), pages 89-96.
    25. James L. Paddock & Daniel R. Siegel & James L. Smith, 1988. "Option Valuation of Claims on Real Assets: The Case of Offshore Petroleum Leases," The Quarterly Journal of Economics, Oxford University Press, vol. 103(3), pages 479-508.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Lorenzo Reus, 2020. "Efficient selection of copper sales contracts for small‐ and medium‐sized mining," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 624-630, June.
    2. Gilani, Seyyed-Omid & Sattarvand, Javad & Hajihassani, Mohsen & Abdullah, Shahrum Shah, 2020. "A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty," Resources Policy, Elsevier, vol. 68(C).

    More about this item


    Mining incidents; Optimal policy; Stochastic dual dynamic programming; Risk-aversion; CVaR; Julia language;

    JEL classification:

    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:63:y:2019:i:c:43. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.