A Clustering Algorithm for Block-Cave Production Scheduling

Abstract

Production scheduling is one of the most important steps in the block-caving design process. Optimum production scheduling could add significant value to a mining project. The goal of long-term mine production scheduling is to determine the mining sequence, which optimizes the company’s strategic objectives while honouring the operational limitations over the mine life. Mathematical programming with exact solution methods is considered a practical tool to model block-caving production scheduling problems; this tool makes it possible to search for the optimum values while considering all of the constraints involved in the operation. This kind of model seeks to account for real-world conditions and must respond to all practical problems which extraction procedures face. Consequently, the number of subjected constraints is considerable and has tighter boundaries, solving the model is not possible or requires a lot of time. It is thus crucial to reduce the size of the problem meaningfully by using techniques which ensure that the absolute solution has less deviation from the original model. This paper presents a clustering algorithm to reduce the size of the large-scale models in order to solve the problem in a reasonable time. The results show a significant reduction in the size of the model and CPU time. Application and comparison of the production schedule based on the draw control system with the clustering technique is presented using 2,487 drawpoints to be extracted over 32 years.