A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty
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 uncertain...
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| Format: | Article |
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Elsevier Ltd
2020
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| _version_ | 1796865052048883712 |
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| author | Gilani, Seyyed Mid Sattarvand, Javad Hajihassani, Mohsen Abdullah, Shahrum Shah |
| author_facet | Gilani, Seyyed Mid Sattarvand, Javad Hajihassani, Mohsen Abdullah, Shahrum Shah |
| author_sort | Gilani, Seyyed Mid |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T20:51:05Z |
| format | Article |
| id | utm.eprints-90543 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:51:05Z |
| publishDate | 2020 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-905432021-04-30T14:47:56Z http://eprints.utm.my/90543/ A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty Gilani, Seyyed Mid Sattarvand, Javad Hajihassani, Mohsen Abdullah, Shahrum Shah T Technology (General) 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. Elsevier Ltd 2020-10 Article PeerReviewed Gilani, Seyyed Mid and Sattarvand, Javad and Hajihassani, Mohsen and 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, 68 . p. 101738. ISSN 0301-4207 http://dx.doi.org/10.1016/j.resourpol.2020.101738 |
| spellingShingle | T Technology (General) Gilani, Seyyed Mid Sattarvand, Javad Hajihassani, Mohsen Abdullah, Shahrum Shah A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| title | A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| title_full | A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| title_fullStr | A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| title_full_unstemmed | A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| title_short | A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| title_sort | stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty |
| topic | T Technology (General) |
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