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レーンのカットオフグレードモデル×Lerchs-Grossmann アルゴリズム×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198819651992
提唱者K. F. LaneHelmut Lerchs and Israel GrossmannDorit S. Hochbaum
種類Economic optimization framework for ore classificationGraph-theoretic algorithm for pit limit optimizationEfficient algorithm for maximum closure problem
原典Lane, K. F. (1988). The economic definition of ore: cutoff grades in theory and practice. Mining Journal Books, London. link ↗Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54. 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Lane Model, Cut-off Grade Optimization, Lane's Optimization ModelLerchs-Grossmann Method, LG AlgorithmPseudoflow Algorithm, Hochbaum Algorithm
関連343
概要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 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.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: Cut-off Grade (Lane) · Lerchs-Grossmann Algorithm · Pseudoflow. 2026-06-18に以下より取得 https://scholargate.app/ja/compare