The problem of long-term open pit mine planning is a large combinatorial problem, which can't be solved easily by mathematical programming models because of the large problem size. In this paper a new metaheuristic algorithm, which has been developed based on the ant colony optimisation (ACO) and its efficiency, have been discussed. To apply the ACO process to a mine planning problem, a series of variables is considered for each block as the pheromone trails that represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. During implementation several mine schedules are constructed in each iteration. Then the pheromone values of all blocks are reduced to a certain percentage and additionally the pheromone value of those blocks that are used in defining the constructed schedules are increased according to the quality of the generated solutions. By repeated iterations, the pheromone values of those blocks that define the shape of the optimum solution are increased, whereas those of the others have been significantly evaporated. By using this algorithm a high degree of complexities could be considered in objective function and constraints of optimisation model._x000D_
In addition it can improve the value of the initial mining schedule up to 34 per cent for some cases in a reasonable computational time.