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Lerchs-Grossmann 알고리즘×레인(Lane)의 절단 등급 모델×슈도플로우 알고리즘×
분야광산공학광산공학광산공학
계열Process / pipelineProcess / pipelineProcess / pipeline
기원 연도196519881992
창시자Helmut Lerchs and Israel GrossmannK. F. LaneDorit S. Hochbaum
유형Graph-theoretic algorithm for pit limit optimizationEconomic optimization framework for ore classificationEfficient algorithm for maximum closure problem
원전Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54. link ↗Lane, K. F. (1988). The economic definition of ore: cutoff grades in theory and practice. Mining Journal Books, London. link ↗Hochbaum, D. S. (1992). A new-old algorithm for minimum-cut and maximum-flow problems. Journal of the ACM, 1(1), 76-109. link ↗
별칭Lerchs-Grossmann Method, LG AlgorithmLane Model, Cut-off Grade Optimization, Lane's Optimization ModelPseudoflow Algorithm, Hochbaum Algorithm
관련433
요약The Lerchs-Grossmann Algorithm is a graph-theoretic method for determining the ultimate pit limit in open-pit mining operations. Introduced by Helmut Lerchs and Israel Grossmann in 1965, it maximizes the net present value of extracted ore while respecting slope stability constraints. This algorithm forms the theoretical foundation for most modern pit optimization software.Lane's Cut-off Grade Model, developed by Kenneth F. Lane and formalized in his 1988 book, provides a rigorous economic framework for determining the minimum grade at which ore should be mined and processed. It accounts for variable mining costs, metallurgical recovery, and commodity prices to optimize profit per unit processed. The model is foundational in mining economics and underpins daily operational decisions at thousands of mines worldwide.The Pseudoflow Algorithm, developed by Dorit Hochbaum in 1992, is a polynomial-time algorithm for computing maximum weighted closures in directed acyclic graphs. In mining, it solves the ultimate pit limit problem more efficiently than earlier methods. By maintaining feasible pseudoflows and iteratively eliminating negative-cost nodes, it achieves near-optimal practical performance even on industrial-scale block models.
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ScholarGate방법 비교: Lerchs-Grossmann Algorithm · Cut-off Grade (Lane) · Pseudoflow. 2026-06-18에 다음에서 검색함: https://scholargate.app/ko/compare