IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v86y2023ipbs0301420723008474.html
   My bibliography  Save this article

Connecting planning horizons in mining complexes with reinforcement learning and stochastic programming

Author

Listed:
  • Levinson, Zachary
  • Dimitrakopoulos, Roussos

Abstract

Connecting short- and long-term production schedules in mining complexes is essential to ensure that the long-term production schedule is achievable at shorter timescales. Previous research that addresses optimizing mining complexes under uncertainty focus on simultaneously optimizing different components in the mining complex to capitalize on advantageous synergies. Typically, short- and long-term production schedules are optimized separately in a number of stages. This poses risk of schedule misalignment, which can adversely affect the economic outcome of a mining complex and the ability to meet long-term production forecasts at shorter timescales. A framework is proposed to jointly optimize short- and long-term production schedules by connecting planning horizons with stochastic mathematical programming and reinforcement learning. The solution approach is tested in a large operating copper mining complex and demonstrates significant improvements in the resulting production and financial forecasts.

Suggested Citation

  • Levinson, Zachary & Dimitrakopoulos, Roussos, 2023. "Connecting planning horizons in mining complexes with reinforcement learning and stochastic programming," Resources Policy, Elsevier, vol. 86(PB).
  • Handle: RePEc:eee:jrpoli:v:86:y:2023:i:pb:s0301420723008474
    DOI: 10.1016/j.resourpol.2023.104136
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723008474
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.resourpol.2023.104136?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. Amina Lamghari & Roussos Dimitrakopoulos & Jacques A Ferland, 2014. "A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(9), pages 1305-1314, September.
    2. 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).
    3. 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.
    4. Luis Montiel & Roussos Dimitrakopoulos, 2017. "A heuristic approach for the stochastic optimization of mine production schedules," Journal of Heuristics, Springer, vol. 23(5), pages 397-415, October.
    5. Lamghari, Amina & Dimitrakopoulos, Roussos, 2012. "A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty," European Journal of Operational Research, Elsevier, vol. 222(3), pages 642-652.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    2. Paithankar, Amol & Chatterjee, Snehamoy & Goodfellow, Ryan, 2021. "Open-pit mining complex optimization under uncertainty with integrated cut-off grade based destination policies," Resources Policy, Elsevier, vol. 70(C).
    3. Franco-Sepúlveda, Giovanni & Del Rio-Cuervo, Juan Camilo & Pachón-Hernández, María Angélica, 2019. "State of the art about metaheuristics and artificial neural networks applied to open pit mining," Resources Policy, Elsevier, vol. 60(C), pages 125-133.
    4. Ashish Kumar & Roussos Dimitrakopoulos & Marco Maulen, 2020. "Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1795-1811, October.
    5. Samavati, Mehran & Essam, Daryl & Nehring, Micah & Sarker, Ruhul, 2017. "A methodology for the large-scale multi-period precedence-constrained knapsack problem: an application in the mining industry," International Journal of Production Economics, Elsevier, vol. 193(C), pages 12-20.
    6. 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.
    7. Devendra Joshi & Marwan Ali Albahar & Premkumar Chithaluru & Aman Singh & Arvind Yadav & Yini Miro, 2022. "A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    8. 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).
    9. Zhang, Jian & Nault, Barrie R. & Dimitrakopoulos, Roussos G., 2019. "Optimizing a mineral value chain with market uncertainty using benders decomposition," European Journal of Operational Research, Elsevier, vol. 274(1), pages 227-239.
    10. Rimélé, Adrien & Dimitrakopoulos, Roussos & Gamache, Michel, 2020. "A dynamic stochastic programming approach for open-pit mine planning with geological and commodity price uncertainty," Resources Policy, Elsevier, vol. 65(C).
    11. Furtado e Faria, Matheus & Dimitrakopoulos, Roussos & Lopes Pinto, Cláudio Lúcio, 2022. "Integrated stochastic optimization of stope design and long-term underground mine production scheduling," Resources Policy, Elsevier, vol. 78(C).
    12. Zhang, Jian & Dimitrakopoulos, Roussos G., 2017. "A dynamic-material-value-based decomposition method for optimizing a mineral value chain with uncertainty," European Journal of Operational Research, Elsevier, vol. 258(2), pages 617-625.
    13. Lamghari, Amina & Dimitrakopoulos, Roussos, 2016. "Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty," European Journal of Operational Research, Elsevier, vol. 250(1), pages 273-290.
    14. Del Castillo, Maria Fernanda & Dimitrakopoulos, Roussos, 2016. "A multivariate destination policy for geometallurgical variables in mineral value chains using coalition-formation clustering," Resources Policy, Elsevier, vol. 50(C), pages 322-332.
    15. Ivorra, Benjamin & Mohammadi, Bijan & Manuel Ramos, Angel, 2015. "A multi-layer line search method to improve the initialization of optimization algorithms," European Journal of Operational Research, Elsevier, vol. 247(3), pages 711-720.
    16. Friedrich-Wilhelm Wellmer & Roland W. Scholz, 2018. "What Is the Optimal and Sustainable Lifetime of a Mine?," Sustainability, MDPI, vol. 10(2), pages 1-22, February.
    17. Amina Lamghari & Roussos Dimitrakopoulos & Jacques Ferland, 2015. "A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines," Journal of Global Optimization, Springer, vol. 63(3), pages 555-582, November.
    18. Chatterjee, Snehamoy & Sethi, Manas Ranjan & Asad, Mohammad Waqar Ali, 2016. "Production phase and ultimate pit limit design under commodity price uncertainty," European Journal of Operational Research, Elsevier, vol. 248(2), pages 658-667.
    19. Biswas, Pritam & Sinha, Rabindra Kumar & Sen, Phalguni, 2023. "A review of state-of-the-art techniques for the determination of the optimum cut-off grade of a metalliferous deposit with a bibliometric mapping in a surface mine planning context," Resources Policy, Elsevier, vol. 83(C).
    20. Nesbitt, Peter & Blake, Lewis R. & Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo K. & Newman, Alexandra & Brickey, Andrea, 2021. "Underground mine scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 294(1), pages 340-352.

    Corrections

    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:86:y:2023:i:pb:s0301420723008474. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.