IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v50y2016icp322-332.html
   My bibliography  Save this article

A multivariate destination policy for geometallurgical variables in mineral value chains using coalition-formation clustering

Author

Listed:
  • Del Castillo, Maria Fernanda
  • Dimitrakopoulos, Roussos

Abstract

Complex polymetallic mining projects with multiple processing streams tend to require tight blending constraints, with different operational and processing targets. These blending requirements are generally not focused solely on metal grade, but rather on a set of geometallurgical variables that affect the performance of the operation and its ability to meet targets and maximize project value. Because of this, a multivariate destination policy is developed here, based on coalition formation clustering (a line of study of cooperative game theory), which avoids the use of cut-off grades and defines where material is sent by accounting for the value and relation of groups of blocks being processed together. This allows improving investment decisions as a result of optimizing project performance, because the variables that affect blending and processing requirements are actively accounted for in the optimization process. A case study on a copper-gold mine with six destinations is presented, where the method proposed shows significant improvements in meeting processing requirements and increases the expected net present value by 5.6% when compared to a traditional method. This shows that complex processing requirements can be accounted for and respected without any loss of project value.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jrpoli:v:50:y:2016:i:c:p:322-332
    DOI: 10.1016/j.resourpol.2016.10.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420716303324
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2016.10.003?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. Vladislav Kargin, 2005. "Uncertainty Of The Shapley Value," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 517-529.
    2. Thompson, Matt & Barr, Drew, 2014. "Cut-off grade: A real options analysis," Resources Policy, Elsevier, vol. 42(C), pages 83-92.
    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. AUMANN, Robert J. & DREZE, Jacques H., 1974. "Cooperative games with coalition structures," LIDAM Reprints CORE 217, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Ramazan, Salih, 2007. "The new Fundamental Tree Algorithm for production scheduling of open pit mines," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1153-1166, March.
    6. Asad, Mohammad Waqar Ali & Dimitrakopoulos, Roussos, 2013. "A heuristic approach to stochastic cutoff grade optimization for open pit mining complexes with multiple processing streams," Resources Policy, Elsevier, vol. 38(4), pages 591-597.
    7. Figueiredo, Paulo N. & Piana, Janaina, 2016. "When “one thing (almost) leads to another”: A micro-level exploration of learning linkages in Brazil's mining industry," Resources Policy, Elsevier, vol. 49(C), pages 405-414.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zheng, Xiaolei & Nguyen, Hoang & Bui, Xuan-Nam, 2021. "Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model," Resources Policy, Elsevier, vol. 74(C).
    2. Nelis, Gonzalo & Morales, Nelson & Jelvez, Enrique, 2023. "Optimal mining cut definition and short-term open pit production scheduling under geological uncertainty," Resources Policy, Elsevier, vol. 81(C).
    3. Yasrebi, Amir Bijan & Hezarkhani, Ardeshir & Afzal, Peyman, 2017. "Application of Present Value-Volume (PV-V) and NPV-Cumulative Total Ore (NPV-CTO) fractal modelling for mining strategy selection," Resources Policy, Elsevier, vol. 53(C), pages 384-393.
    4. Kloeckner, Jonas & Alves, João Lucas O. & Silva, Flavio H.T. & Guimaraes, Octavio R.A. & Bassani, Marcel A.A. & Costa, Joao Felipe C.L., 2021. "Application of risk assessment to improve sustainability in bauxite mining," Resources Policy, Elsevier, vol. 74(C).

    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. 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).
    2. Rahimi, Esmaeil & Akbari, Afshin, 2016. "Application of KKT in determining the final destination of mined material in multi-processing mines," Resources Policy, Elsevier, vol. 50(C), pages 10-18.
    3. Jyrki Savolainen & Ramin Rakhsha & Richard Durham, 2022. "Simulation-based decision-making system for optimal mine production plan selection," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(2), pages 267-281, June.
    4. 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.
    5. Asad, Mohammad Waqar Ali & Qureshi, Muhammad Asim & Jang, Hyongdoo, 2016. "A review of cut-off grade policy models for open pit mining operations," Resources Policy, Elsevier, vol. 49(C), pages 142-152.
    6. Moreno, Eduardo & Rezakhah, Mojtaba & Newman, Alexandra & Ferreira, Felipe, 2017. "Linear models for stockpiling in open-pit mine production scheduling problems," European Journal of Operational Research, Elsevier, vol. 260(1), pages 212-221.
    7. 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.
    8. Lin, Jingsi & Asad, Mohammad Waqar Ali & Topal, Erkan & Chang, Ping & Huang, Jinxin & Lin, Wei, 2024. "A novel model for sustainable production scheduling of an open-pit mining complex considering waste encapsulation," Resources Policy, Elsevier, vol. 91(C).
    9. 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).
    10. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung & Bui, Thu-Thuy & Nguyen, Nga & Vu, Diep-Anh & Mahesh, Vinyas & Moayedi, Hossein, 2020. "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm," Resources Policy, Elsevier, vol. 66(C).
    11. Savolainen, Jyrki, 2016. "Real options in metal mining project valuation: Review of literature," Resources Policy, Elsevier, vol. 50(C), pages 49-65.
    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. 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).
    14. Ahmadi, Mohammad Reza & Shahabi, Reza Shakoor, 2018. "Cutoff grade optimization in open pit mines using genetic algorithm," Resources Policy, Elsevier, vol. 55(C), pages 184-191.
    15. Sylvain Béal & Marc Deschamps & Mostapha Diss & Rodrigue Tido Takeng, 2024. "Cooperative games with diversity constraints," Working Papers hal-04447373, HAL.
    16. Caulier, Jean-François & Mauleon, Ana & Vannetelbosch, Vincent, 2015. "Allocation rules for coalitional network games," Mathematical Social Sciences, Elsevier, vol. 78(C), pages 80-88.
    17. Rafael Epstein & Marcel Goic & Andrés Weintraub & Jaime Catalán & Pablo Santibáñez & Rodolfo Urrutia & Raúl Cancino & Sergio Gaete & Augusto Aguayo & Felipe Caro, 2012. "Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines," Operations Research, INFORMS, vol. 60(1), pages 4-17, February.
    18. 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.
    19. Lukáš Adam & Tomáš Kroupa, 2017. "The intermediate set and limiting superdifferential for coalitional games: between the core and the Weber set," International Journal of Game Theory, Springer;Game Theory Society, vol. 46(4), pages 891-918, November.
    20. Antonio Magaña & Francesc Carreras, 2018. "Coalition Formation and Stability," Group Decision and Negotiation, Springer, vol. 27(3), pages 467-502, June.

    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:50:y:2016:i:c:p:322-332. 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.