Using a Combination of Genetic Algorithm and the Grid Search Method to Determine Optimum Cutoff Grades of Multiple Metal Deposits

Evolutionary optimisation of coal production in underground mines …

“…Like any technique evolutionary algorithms have advantages and disadvantages, but because of easy development and implementation, the possibility of multi-criteria optimisation and easy collaboration with other techniques (heuristics) their popularity nowadays has grown. Evolutionary algorithms have been already used in the mine planning issues [1,12,13,15,17,24,31,33,42,43], more often for open-pits. Use of evolutionary algorithms in mine planning allows to take into account most of the problem constraints and specific conditions.…”

Receding horizon control applied to optimal mine planning

“…Mining strategies need to satisfy a number of constraints on the order that material can be mined -see [10,7,12,13,16,17,15,14,25,5,6,9,24]. In the usual MILP formulation these constraints are typically included as a set of precedence constraints.…”

“…Indeed, there exists a substantial, and growing, literature on this topicsee, for example, [10,7,12,13,16,17,15,14,25,5,6,9]. Also, several commercial software packages are available which can be used for mine planning -see [1][2][3][4].…”

“…Typical procedures, e.g., [24] formulate the problem as a mixed integer linear programme (MILP). A variety of solution strategies have been used including the commercially available software CPLEX, branch and cut methods [10], genetic algorithms [7,[12][13][14][15][16][17], simulated annealing [25], etc.…”

A Genetic Algorithm with Zooming for the Determination of the Optimal Open Pit Mines Layout

“…Whilst the application of stochastic search methodologies is not new both in slope stability [16,17] and mining engineering [18,19], the GA proposed here is a non-standard one and allows a relatively quick convergence to the optimal solution showing excellent stability and robustness, both in case of problems with many variables and enforcing the GA to work with small populations (micro-GA).…”