ABSTRACT

Conventional mine planning often relies on parameters estimation to obtain a single production plan. There is no guarantee that these estimations will be accurate in the long term, and this could lead to issues in the mine operation. To deal with this uncertainty, different optimization models have been proposed, which incorporate equally probable scenarios. Unfortunately, the incorporation of uncertainty also imposes a computational challenge: a large number of scenarios is desirable to capture the variability of the uncertain parameters, but each additional scenario increases the computational complexity of the mathematical problem, limiting the cases that can be addressed with stochastic optimization. This paper implements a variance reduction technique in the sequential Gaussian simulation algorithm, which generates grade scenarios with negative correlation, so fewer scenarios can be used without compromising the representation of the grade variability. Our experiments show that using these scenarios achieves the same precision in the objective value of a stochastic optimization problem, but using fewer simulations in the formulation compared with the conventional gaussian algorithm.