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Lerchs-Grossmann アルゴリズム×レーンのカットオフグレードモデル×擬似フローアルゴリズム×
分野鉱山工学鉱山工学鉱山工学
系統Process / pipelineProcess / pipelineProcess / pipeline
提唱年196519881992
提唱者Helmut Lerchs and Israel GrossmannK. F. LaneDorit S. Hochbaum
種類Graph-theoretic algorithm for pit limit optimizationEconomic optimization framework for ore classificationEfficient algorithm for maximum closure problem
原典Lerchs, 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 ↗
別名Lerchs-Grossmann Method, LG AlgorithmLane Model, Cut-off Grade Optimization, Lane's Optimization ModelPseudoflow Algorithm, Hochbaum Algorithm
関連433
概要The 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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ScholarGate手法を比較: Lerchs-Grossmann Algorithm · Cut-off Grade (Lane) · Pseudoflow. 2026-06-18に以下より取得 https://scholargate.app/ja/compare