Long-Term Open-Pit Mine Planning with Large Neighbourhood Search
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915325670326272 |
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| author | Blom, Michelle Pearce, Adrian R. Cote, Pascal |
| author_facet | Blom, Michelle Pearce, Adrian R. Cote, Pascal |
| contents | We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach is able to find near-optimal solutions to problems that cannot be solved by an off-the-shelf solver in a reasonable time frame, or with reasonable computational resources. Our method substantially reduces the solve times required for large models, allowing mine planners to explore multiple scenarios in a timely fashion. Our approach is being used by Rio Tinto to solve large long-term mine planning problems, and has been responsible for generating millions of dollars in value insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long-Term Open-Pit Mine Planning with Large Neighbourhood Search Blom, Michelle Pearce, Adrian R. Cote, Pascal Optimization and Control We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach is able to find near-optimal solutions to problems that cannot be solved by an off-the-shelf solver in a reasonable time frame, or with reasonable computational resources. Our method substantially reduces the solve times required for large models, allowing mine planners to explore multiple scenarios in a timely fashion. Our approach is being used by Rio Tinto to solve large long-term mine planning problems, and has been responsible for generating millions of dollars in value insights. |
| title | Long-Term Open-Pit Mine Planning with Large Neighbourhood Search |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2403.18213 |