Evaluating the Effect of Block Aggregation Approach on Ultimate Pit Limit Characteristics Using the Linear Programming Model

An open-pit mine production planning begins with determining the ultimate pit limit of an open-pit mine. The ultimate pit limit solver selects blocks whose total economic value is maximum while meeting the slope constraints. In other words, a group of blocks that maximize a selected parameter, such as profit, metal content, or net present value, is considered in determining the ultimate pit limit. Also, the ultimate pit limit is designed to select the waste dump location, surface facilities, extractable reserves, and the amount of waste removal. The production planning problem in large-scale open-pit mines is referred to as an NP-hard problem because it cannot be solved in a reasonable computational time. To solve this, various methods, including aggregation methods, have been proposed to reduce the size of the issue. In this paper, to evaluate the efficiency of the block aggregation technique based on the pit values and computational times, at first, the heuristic Tabesh-AskariNasab aggregation algorithm was applied to the block models with 2400 and 11400 blocks. Then the ultimate pit limit based on the original block model and reconstructed block models were determined using the linear programming model. Comparing the results in both block models indicates that the block aggregation approach considerably decreased computational time while generating near-optimal pit values. These results are more critical in large-scale production planning problems, exactly in open pit mine scheduling. Furthermore, the slope of pit walls was decreased by increasing the size of clusters, and the stripping ratio increased in both block models.