Pit limit optimization using stochastic process

So far, a large number of algorithms have been developed for the optimization of pit limits, most of which follow deterministic rules. In this paper, a new algorithm is introduced, which follows a probabilistic logic using the Markov chain process. The algorithm is implemented on a transition matrix that corresponds to the 2D conventional economic block model of the mining area. The probability of mining a block is proportional to the desirability it may provide. Applying this algorithm, the probability of mining each block is obtained, and finally, the optimum pit limits are defined as the pit, which provides the highest probability of mining. In order to validate the proposed model, a numerical example is given and the results of the algorithm are compared to those of a dynamic programming algorithm. A 2D analysis of the problem is discussed in this paper. Although dealing with 3D problems requires larger size matrices, the optimization method is the same and the algorithm does not need any smoothing.