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Adaptive simultaneous stochastic optimization of mining complexes: Where is the value coming from?

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  • Dimitrakopoulos, Roussos
  • Del Castillo, Maria Fernanda

Abstract

This paper aims to identify the sources of value created in the strategic plan of a mining complex when the adaptive simultaneous stochastic optimization of mining complex (ASSOMC) approach is used. This approach considers operational and investment alternatives dynamically within the simultaneous stochastic optimization of mining complex (SSOMC) framework, providing an adaptive strategic plan that manages technical risk and maximizes value. A case study on a world-class mining complex illustrates the effects of this optimization model, comparing the adaptive alternatives of the ASSOMC with the fixed SSOMC case. Results show that considering the SSOMC without alternatives was a starting point, including either investment or operational alternatives in a fixed manner, provides an increase in NPV of 4.4 % and 2.8 %, respectively; whereas considering both jointly increases the NPV by 10.3 %. On the other hand, when adaptive changes are considered over investments, such as additional crushers or conveyor belts, the NPV increases further, by about 20 %. The focus is placed on identifying the location and components where this extra value is created within the mining complex, understanding the effect that the alternatives have, and capitalizing from them. This study finds that, due to the non-linear synergies that exist between the different components of a mining complex, the adaptive aspect of the approach allows the production plan optimization to be proactive and to tailor its configuration according to possible changes and future developments.

Suggested Citation

  • Dimitrakopoulos, Roussos & Del Castillo, Maria Fernanda, 2025. "Adaptive simultaneous stochastic optimization of mining complexes: Where is the value coming from?," Resources Policy, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:jrpoli:v:103:y:2025:i:c:s0301420725000911
    DOI: 10.1016/j.resourpol.2025.105549
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    References listed on IDEAS

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    1. Levinson, Zachary & Dimitrakopoulos, Roussos, 2023. "Connecting planning horizons in mining complexes with reinforcement learning and stochastic programming," Resources Policy, Elsevier, vol. 86(PB).
    2. Del Castillo, M. Fernanda & Dimitrakopoulos, Roussos, 2019. "Dynamically optimizing the strategic plan of mining complexes under supply uncertainty," Resources Policy, Elsevier, vol. 60(C), pages 83-93.
    3. Mai, Ngoc Luan & Topal, Erkan & Erten, Oktay & Sommerville, Bruce, 2019. "A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming," Resources Policy, Elsevier, vol. 62(C), pages 571-579.
    4. Paithankar, Amol & Chatterjee, Snehamoy & Goodfellow, Ryan & Asad, Mohammad Waqar Ali, 2020. "Simultaneous stochastic optimization of production sequence and dynamic cut-off grades in an open pit mining operation," Resources Policy, Elsevier, vol. 66(C).
    5. Montiel, Luis & Dimitrakopoulos, Roussos, 2015. "Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach," European Journal of Operational Research, Elsevier, vol. 247(1), pages 166-178.
    6. Yossiri Adulyasak & Jean-François Cordeau & Raf Jans, 2015. "Benders Decomposition for Production Routing Under Demand Uncertainty," Operations Research, INFORMS, vol. 63(4), pages 851-867, August.
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