Stochastic analysis of dig limit optimization using simulated annealing

The results of dig limit delineation in open pit mining are never truly optimized due to gaps the underlying data, such as insufficient sampling. Aside from data uncertainty, there is also an influence on final by either humans or heuristic character optimization method like simulated annealing. Several optimizers have been published, which can replace manual dig-limits designing process. However, these designs generally not adapted account for this character. In paper we present a stochastic analysis tool that be used with dig-limit increase confidence obtained results. First, enhanced annealing algorithm presented. Then, tested ten different blasts at Marigold mine, Nevada, USA, case study. Finally, analysed destination-based ensemble probability map and conducted solution distribution. generated include high revenue areas excluded comparable show improved objective values. provides block destination probabilities box plots distribution opportunity value limit. Furthermore, tool, it possible make well-informed design decisions uncertainty.