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A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty

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  • Gilani, Seyyed-Omid
  • Sattarvand, Javad
  • Hajihassani, Mohsen
  • Abdullah, Shahrum Shah

Abstract

Long term production planning (LTPP) plays a critical role to achieve success of a mining operation. LTPP, as an optimization problem, aims to maximize the net present value (NPV) of a mine subject to a set of constraints. One of the main reasons for not achieving production targets is the uncertainty of the LTPP's inputs. Geological uncertainty as the main sources of uncertainty is considered in this research. In this regard, a set of equiprobable scenarios of orebody and derived two new block model called “risk block model” and “EType” were used as inputs. Then, a stochastic integer programming (SIP) model was developed to integrate the geological uncertainty. Finally, a PSO-based algorithm was developed to solve the SIP model. Four different strategies were developed, according to the population topology and how to use the risk block model. Population topology defines the subset of particles that effect on each particle. Implementation the proposed approach on a large scale mine demonstrate its performance to develop a unique schedule considering geological uncertainties with maximum NPV and minimum risk of not achieving production targets. Investigations show that Gbest based PSO is more susceptible to trap in local optima. Multiple risk based strategies are able to generate better solutions, however, single risk based strategies are good practices when companies are looking for flexible or specific risk based designs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jrpoli:v:68:y:2020:i:c:s0301420720301616
    DOI: 10.1016/j.resourpol.2020.101738
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    References listed on IDEAS

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    Cited by:

    1. Devendra Joshi & Hamed Gholami & Hitesh Mohapatra & Anis Ali & Dalia Streimikiene & Susanta Kumar Satpathy & Arvind Yadav, 2022. "The Application of Stochastic Mine Production Scheduling in the Presence of Geological Uncertainty," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    2. Alipour, Aref & Khodaiari, Ali Asghar & Jafari, Ahmad & Tavakkoli-Moghaddam, Reza, 2022. "An integrated approach to open-pit mines production scheduling," Resources Policy, Elsevier, vol. 75(C).
    3. Javed Mallick & Saeed Alqadhi & Swapan Talukdar & Majed AlSubih & Mohd. Ahmed & Roohul Abad Khan & Nabil Ben Kahla & Saud M. Abutayeh, 2021. "Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    4. Hazrathosseini, Arman & Moradi Afrapoli, Ali, 2023. "The advent of digital twins in surface mining: Its time has finally arrived," Resources Policy, Elsevier, vol. 80(C).
    5. Abu Reza Md. Towfiqul Islam & Swapan Talukdar & Shumona Akhter & Kutub Uddin Eibek & Md. Mostafizur Rahman & Swades Pal & Mohd Waseem Naikoo & Atiqur Rahman & Amir Mosavi, 2022. "Assessing the Impact of the Farakka Barrage on Hydrological Alteration in the Padma River with Future Insight," Sustainability, MDPI, vol. 14(9), pages 1-26, April.
    6. 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).

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