Academic Journal
Long-Term Open-Pit Mine Planning with Large Neighborhood Search.
| Τίτλος: | Long-Term Open-Pit Mine Planning with Large Neighborhood Search. |
|---|---|
| Συγγραφείς: | Blom, Michelle1 (AUTHOR) michelle.blom@unimelb.edu.au, Pearce, Adrian1 (AUTHOR) adrianrp@unimelb.edu.au, Côté, Pascal2 (AUTHOR) Pascal.Cote@riotinto.com |
| Πηγή: | INFORMS Journal on Applied Analytics. Mar/Apr2026, Vol. 56 Issue 2, p115-132. 18p. |
| Θεματικοί όροι: | *Resource management, *Mathematical programming, *Heuristic, *Strip mining, *Mathematical optimization, Mixed integer linear programming, Metaheuristic algorithms, Planning techniques |
| Περίληψη: | We present a large neighborhood search–based approach for solving complex, long-term, open-pit mine planning problems. 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 generate millions of dollars in value insights. We present a large neighborhood 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 neighborhood 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. [ABSTRACT FROM AUTHOR] |
| Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: bsx DbLabel: Business Source Index An: 192598776 RelevancyScore: 1492 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1491.61413574219 |
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| Items | – Name: Title Label: Title Group: Ti Data: Long-Term Open-Pit Mine Planning with Large Neighborhood Search. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Blom%2C+Michelle%22">Blom, Michelle</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> michelle.blom@unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Pearce%2C+Adrian%22">Pearce, Adrian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adrianrp@unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Côté%2C+Pascal%22">Côté, Pascal</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> Pascal.Cote@riotinto.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Applied+Analytics%22">INFORMS Journal on Applied Analytics</searchLink>. Mar/Apr2026, Vol. 56 Issue 2, p115-132. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Resource+management%22">Resource management</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br />*<searchLink fieldCode="DE" term="%22Strip+mining%22">Strip mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Planning+techniques%22">Planning techniques</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present a large neighborhood search–based approach for solving complex, long-term, open-pit mine planning problems. 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 generate millions of dollars in value insights. We present a large neighborhood 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 neighborhood 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/inte.2024.0152 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 115 Subjects: – SubjectFull: Resource management Type: general – SubjectFull: Mathematical programming Type: general – SubjectFull: Heuristic Type: general – SubjectFull: Strip mining Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Planning techniques Type: general Titles: – TitleFull: Long-Term Open-Pit Mine Planning with Large Neighborhood Search. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Blom, Michelle – PersonEntity: Name: NameFull: Pearce, Adrian – PersonEntity: Name: NameFull: Côté, Pascal IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar/Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26440865 Numbering: – Type: volume Value: 56 – Type: issue Value: 2 Titles: – TitleFull: INFORMS Journal on Applied Analytics Type: main |
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