Сравнение методов

	Индекс работы Бонда	Алгоритм Лерхса-Гроссмана	Алгоритм псевдопотока
Область	Горное дело	Горное дело	Горное дело
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1952	1965	1992
Автор метода	Fred C. Bond	Helmut Lerchs and Israel Grossmann	Dorit S. Hochbaum
Тип	Empirical method for grinding energy estimation	Graph-theoretic algorithm for pit limit optimization	Efficient algorithm for maximum closure problem
Основополагающий источник	Bond, F. C. (1952). The third theory of comminution. Transactions of the American Institute of Mining and Metallurgical Engineers, 193, 484-494.	Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54.	Hochbaum, D. S. (1992). A new-old algorithm for minimum-cut and maximum-flow problems. Journal of the ACM, 1(1), 76-109.
Другие названия	Bond Work Index, BWI, Bond Index Test	Lerchs-Grossmann Method, LG Algorithm	Pseudoflow Algorithm, Hochbaum Algorithm
Связанные	3	4	3
Сводка	The Bond Work Index, introduced by Fred C. Bond in 1952, is an empirical parameter that characterizes the resistance of an ore to grinding in a tumbling mill. It is defined as the kilowatt-hours per short ton (kWh/st) of electrical energy required to reduce a coarse ore from theoretically infinite size to 80% passing 100 micrometers. The Bond Index is foundational in mineral processing plant design and cost estimation 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.