A New Metaheuristic Algorithm for Long-Term Open-Pit Production Planning / Nowy meta-heurystyczny algorytm wspomagający długoterminowe planowanie produkcji w kopalni odkrywkowej

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Abstract

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.

Abstract

W artykule zaprezentowano nowy meta-heurystyczny algorytm oparty na zasadach optymalizacji mrowiska i zbadano jego skuteczność w zastosowaniach do planowania wydobycia w kopalniach. Uwzględniono szereg zmiennych w każdym bloku schematu i przeanalizowano „ślady feromonów” które przedstawiają „dążność” poszczególnych bloków w danej kolumnie do stania się najgłębszym punktem kopalni w trakcie określonego okresu prowadzenia prac wydobywczych. W ramach kolejnych iteracji generuje się kilka harmonogramów prowadzenia wydobycia. Następnie wartości poziomu feromonów przypisane do kolejnych bloków redukowane są do wielkości wyrażonych w procentach a wartości poziomu feromonów przypisane do bloków wykorzystywanych do wygenerowania danego harmonogramu zostają powiększone, zgodnie z wymogami odnośnie jakości uzyskanych rozwiązań. Drogą kolejnych iteracji, wartości poziomu feromonów przypisane do bloków generujących rozwiązania optymalne zostają powiększane podczas gdy wartości przypisane do bloków pozostałych zostają odpowiednio pomniejszone.

References
  • Azimi Y., Osanloo M., 2011. Determination of open pit mining cut-off grade strategy using combination of nonlinearprogramming and genetic algorithm. Arch. Min. Sci., Vol. 56, No 2, p. 189-212.

  • Caccetta L., Hill S.P., 2003. An application of branch and cut to open pit mine scheduling. Journal of Global Optimisation, 27, p. 349-365.

  • Caccetta L., Kelsey P., Giannini L., 1998. Open pit mine production scheduling, in Proceedings 3rd Regional APCOM Symposium, p. 65-72 (The Australasian Institute of Mining and Metallurgy: Kalgoorlie).

  • Dagdelen K., Johnson T.B., 1986. Optimum open pit mine production scheduling by Lagrangian parameterization, in Proceedings Application of Computer in Mineral Industry, p. 127-142, (Littleton, Colorado)

  • Denby B., Schofield D., 1994. Open pit design and scheduling by use of genetic algorithms. Trans. Inst. Min. Metall. Sec. A: Min. Industry, 103, A21-A26.

  • Denby B., Schofield D., 1995. Inclusion of risk assessment in open pit design and scheduling. Trans. Inst. Min. Metall. Sec. A: Min. Industry, 104, A67-A71.

  • Denby B., Schofield D., 1996. Genetic algorithms for open pit scheduling-extension into 3-dimentions, in Proceedings Mine Planning and Equipment Selection Conference, Sao Paulo, Brazil, p. 177-185.

  • Denby B., Schofield D., Surme T., 1998. Genetic algorithms for flexible scheduling of open pit operations, in Proceedings 27th Application of Computer in Mineral Industry, p. 473-483, (London).

  • Dorigo M., Stützle T., 2004. Ant Colony Optimisation, p. 67-113, (A Bradford Book).

  • Dowd P.A., Onur A.H., 1992. Optimising open pit design and sequencing, in Proceedings 23rd Application of Computer in Mineral Industry, p. 411-422, (Tucson, Arizona).

  • Kumral, M and Dowd, P A, 2002. Short-term mine production scheduling for industrial minerals using multi-objective simulated annealing, in Proceedings 30th Application of Computer in Mineral Industry, p. 731-742, (Fairbanks, Alaska).

  • Kumral M., Dowd P.A., 2005. A simulated annealing approach to mine production scheduling. Journal of the Operational Research Society, 56, p. 922-930.

  • Lerchs H., Grossmann I.F., 1965. Optimum design of open pit mines. Canadian Institute of Mining Transactions, 68, p. 17-24.

  • Onur A.H., Dowd P.A., 1993. Open pit optimisation-part 2: production scheduling and inclusion of roadways. Trans. Inst. Min. Metall. Sec. A: Min. Industry, 102, p. A105-A113.

  • Osanloo M., Gholamnejad J., Karimi B., 2008. Long-term open pit mine production planning: a review of models andalgorithms. International Journal of Mining, Reclamation and Environment, Vol. 221, p. 3-35.

  • Ramazan S., Dagdelen K., Johnson T.B., 2005. Fundamental tree algorithm in optimising production scheduling foropen pit mine design. Trans. Inst. Min. Metall. Sec. A: Mining Technol., 114, p. A45-A114.

  • Ramazan S., Dimitrakopoulos R., 2004. Stochastic optimisation of long-term production scheduling for open pit mineswith a new integer programming formulation, in Proceedings Orebody Modelling and Strategic Mine Planning Spectrum Series, Vol. 14, p. 359-365.

  • Sattarvand J., 2009. Long-term open-pit planning by ant colony optimisation. PhD thesis, RWTH Aachen University - Germany.

  • Sayadi A., Fathianpour N., Mousavi A.A., 2011. Open pit optimization in 3D using a new artificial neural network. Arch. Min. Sci., Vol. 56, No 3, p. 389-403.

  • Wang Q., Sevim H., 1995. Alternative to parameterisation in finding a series of maximum-metal pits for productionplanning. Mining Engineering, p. 178-182.

Archives of Mining Sciences

The Journal of Committee of Mining of Polish Academy of Sciences

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