Using Clustering Methods for Open Pit Mine Production Scheduling
Typical mine planning process includes creating a mining block model, applying the ultimate pit limit analysis and creating mining cuts for production scheduling. Mixed integer linear programming (MILP) has been used extensively for optimal mine production scheduling of open pit mines. One main obst...
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| Other Authors: | |
| Format: | Journal Article |
| Published: |
The Australasian Institute of Mining and Metallurgy
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/34260 |
| _version_ | 1848754174021337088 |
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| author | Ren, H. Topal, Erkan |
| author2 | Paul Hagan |
| author_facet | Paul Hagan Ren, H. Topal, Erkan |
| author_sort | Ren, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Typical mine planning process includes creating a mining block model, applying the ultimate pit limit analysis and creating mining cuts for production scheduling. Mixed integer linear programming (MILP) has been used extensively for optimal mine production scheduling of open pit mines. One main obstacle for large scale mine scheduling is the size of the problem. In large scale open pit mines, the number of blocks are too numerous to allow an optimal solution to be developed in a reasonable amount of time frame. However, the problem can be simplified by aggregation of mining blocks to create the mining cuts. The objective of this research is to analyse and validate a clustering algorithm for mining block aggregation. Popular cluster algorithms are reviewed and compared for the effectiveness of clustering for production scheduling purposes. Fuzzy C-Means (FCM) cluster method is used to partition mining benches and it is tested against the efficiency and practicality of mine production scheduling. The block aggregation algorithm is validated with a case study of a copper deposit. The net present value (NPV) of the production schedule which is created by using clustering algorithm is $2.25 M higher than the traditional pushback based production schedule. |
| first_indexed | 2025-11-14T08:36:13Z |
| format | Journal Article |
| id | curtin-20.500.11937-34260 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:36:13Z |
| publishDate | 2014 |
| publisher | The Australasian Institute of Mining and Metallurgy |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-342602017-02-28T01:38:49Z Using Clustering Methods for Open Pit Mine Production Scheduling Ren, H. Topal, Erkan Paul Hagan Typical mine planning process includes creating a mining block model, applying the ultimate pit limit analysis and creating mining cuts for production scheduling. Mixed integer linear programming (MILP) has been used extensively for optimal mine production scheduling of open pit mines. One main obstacle for large scale mine scheduling is the size of the problem. In large scale open pit mines, the number of blocks are too numerous to allow an optimal solution to be developed in a reasonable amount of time frame. However, the problem can be simplified by aggregation of mining blocks to create the mining cuts. The objective of this research is to analyse and validate a clustering algorithm for mining block aggregation. Popular cluster algorithms are reviewed and compared for the effectiveness of clustering for production scheduling purposes. Fuzzy C-Means (FCM) cluster method is used to partition mining benches and it is tested against the efficiency and practicality of mine production scheduling. The block aggregation algorithm is validated with a case study of a copper deposit. The net present value (NPV) of the production schedule which is created by using clustering algorithm is $2.25 M higher than the traditional pushback based production schedule. 2014 Journal Article http://hdl.handle.net/20.500.11937/34260 The Australasian Institute of Mining and Metallurgy restricted |
| spellingShingle | Ren, H. Topal, Erkan Using Clustering Methods for Open Pit Mine Production Scheduling |
| title | Using Clustering Methods for Open Pit Mine Production Scheduling |
| title_full | Using Clustering Methods for Open Pit Mine Production Scheduling |
| title_fullStr | Using Clustering Methods for Open Pit Mine Production Scheduling |
| title_full_unstemmed | Using Clustering Methods for Open Pit Mine Production Scheduling |
| title_short | Using Clustering Methods for Open Pit Mine Production Scheduling |
| title_sort | using clustering methods for open pit mine production scheduling |
| url | http://hdl.handle.net/20.500.11937/34260 |