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Abstract One of the most difficult problems in mining operation is how to determine optimum cutoff grades of ores at different periods over the lifespan of the mine that will maximize the net present value (NPV) of the mine. Maximizing the NPV of a mining operation, subject to different constraints is a non-linear programming problem. These problems can often be solved by the use of gradient methods, direct search methods or intelligent optimization methods. In this paper, a hybrid genetic algorithm combined with the grid search method is used to find the optimum cutoff grades of multiple metal deposits that will maximize the NPV. At first, the solution space is determined by using the grid search method, then the optimum cutoff grades are determined accurately by the use of genetic algorithm. The result of a sensitivity analysis of the problem shows that when crossover probability ranges between 55 to 70%, and mutation probability is between 8 to 11%, and population size greater than 35 individuals; the optimum cutoff grades can be determined with high accuracy while keeping the computational time relatively low.
Ataei et al. (2003) studied this question.