A New Metaheuristic Algorithm for Long-Term Open-Pit Production Planning / Nowy meta-heurystyczny algorytm wspomagający długoterminowe planowanie produkcji w kopalni odkrywkowej
作者： Javad Sattarvand , Christian Niemann-Delius
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摘要: Paper describes 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 on mine planning problem, a series of variables are 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.
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