Long-Term Open-Pit Mine Planning with Large Neighborhood Search

Michelle Blom (), Adrian Pearce () and Pascal Côté ()
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Michelle Blom: School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia
Adrian Pearce: School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia
Pascal Côté: Operations Research and Decision Science, Rio Tinto, Montreal, Quebec H3B 0E3, Canada

Interfaces, 2026, vol. 56, issue 2, 152-132

Abstract: 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.

Keywords: open-pit mining; long-term planning; large neighborhood search (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:56:y:2026:i:2:p:152-132

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