However, the requirement of a payment to retrieve this information might render the total value negative, causing a need to valuate this future information properly. In terms of the economic utility theory, Schlee ( 1991) proves that perfect information always has a positive value. The more common term for this problem used across these various industries is the valuation of future information (under uncertainty). Optimizing an infill drilling pattern can be linked to a more general problem present in many different applications such as stock markets, research effort direction, project development, and others. The results of the proposed method demonstrate its practical aspects and its effectiveness towards the optimization of infill drilling schemes. Material type changes are the driver for changes in the extraction sequence, which ultimately defines the value of a mining operation. The best pattern is defined in terms of causing the most material type changes for the blocks in the stockpile. In several mining periods grade targets of deleterious elements at the processing plant can only be met by using high amounts of stockpiled material. The stockpiles in this mining complex are of particular interest due to difficult-to-meet blending requirements. The proposed method is applied to a long-term, multi-element stockpile, which is a part of a gold mining complex. The MAB optimizes the best infill drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. The method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. The two questions that always arise upon making this decision are whether more drilling is required and, if so, where the additional drill holes should be located. Mining operations face a decision regarding additional drilling several times during their lifetime.
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