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] |
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