A Comparative Study of Algorithms for Solving the Multiobjective Open-Pit Mining Operational Planning Problems

  • Rafael Frederico Alexandre
  • Felipe Campelo
  • Carlos M. Fonseca
  • João Antônio de Vasconcelos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9019)


This work presents a comparison of results obtained by different methods for the Multiobjective Open-Pit Mining Operational Planning Problem, which consists of dynamically and efficiently allocating a fleet of trucks with the goal of maximizing the production while reducing the number of trucks in operation, subject to a set of constraints defined by a mathematical model. Three algorithms were used to tackle instances of this problem: NSGA-II, SPEA2 and an ILS-based multiobjective optimizer called MILS. An expert system for computational simulation of open pit mines was employed for evaluating solutions generated by the algorithms. These methods were compared in terms of the quality of the solution sets returned, measured in terms of hypervolume and empirical attainment function (EAF). The results are presented and discussed.


Open pit mines Dispatch Multiobjective optimization Performance comparison 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alexandre, R., Vasconcelos, J., Campelo, F.: Additional electronic files. http://cpdee.ufmg.br/~fcampelo/files/MOPMOPP/ (2014)
  2. 2.
    Chicano, F., Alba, E.: Exact computation of the expectation curves of the bit-flip mutation using landscapes theory. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, Dublin, Ireland, pp. 2027–2034 (July 2011)Google Scholar
  3. 3.
    Coelho, V., Souza, M., Coelho, I., Guimarães, F., Lust, T., Cruz, R.C.: Multi-objective approaches for the open-pit mining operational planning problem. Electronic Notes in Discrete Mathematics 39, 233–240 (2012)CrossRefGoogle Scholar
  4. 4.
    Coello, C., Lamont, G., Veldhuizen, D.: Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine 1(1), 28–36 (2006)CrossRefGoogle Scholar
  5. 5.
    Coello, C., Lamont, G., Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-Objective Problem, 2nd edn. Springer (2007)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Evolutionary Computation 6(2), 182–187 (2002)CrossRefGoogle Scholar
  7. 7.
    Dias, A., Vasconcelos, J.: Multiobjective genetic algorithms applied to solve optimization problems. IEEE Transactions on Magnetics 38(2), 1133–1136 (2001)CrossRefGoogle Scholar
  8. 8.
    Doig, P., Kizil, M.: Improvements in truck requirement estimations using detailed haulage analysis. In: 3th Coal Operators Conference, The Australasian Institute of Mining and Metallurgy and Mine Managers Association of Australia, pp. 368–375 (February 2013)Google Scholar
  9. 9.
    Feo, T., Resende, M.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6(2), 109–133 (1995)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Fonseca, C.M., da Fonseca, V.G., Paquete, L.: Exploring the Performance of Stochastic Multiobjective Optimisers with the Second-Order Attainment Function. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 250–264. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  11. 11.
    Geiger, M.: The PILS metaheuristic and its application to multi-objective machine scheduling. In: Kfer, K.H., Rommelfanger, H., Tammer, C., Winkler, K. (eds.) Multicriteria Decision Making and Fuzzy Systems Theory, Methods and Applications. pp. 43–58. Shaker Verlag, Industriemathematik und Angewandte Mathematik (2006)Google Scholar
  12. 12.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley (1989)Google Scholar
  13. 13.
    Hansen, P., Mladenovic, N., Pérez, J.M.: Variable neighbourhood search: methods and applications. 4OR 6(4), 319–360 (2008)Google Scholar
  14. 14.
    He, M., Wei, J., Lu, X., Huang, B.: The genetic algorithm for truck dispatching problems in surface mine. Information Technology Journal 9, 710–714 (2010)CrossRefGoogle Scholar
  15. 15.
    Ibáñez, M., Stützle, T., Paquete, L.: Graphical tools for the analysis of bi-objective optimization algorithms. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2010, pp. 1959–1962. ACM, New York (2010)Google Scholar
  16. 16.
    ILOG: Users Manual. IBM (2008)Google Scholar
  17. 17.
    Kelton, W., Sadowski, R., Sturrock, D.: Simulation with Arena. McGraw-Hill series in industrial engineering and management science, 4. ed. internat. ed. McGraw-Hill Higher Education, Boston (2007)Google Scholar
  18. 18.
    Loureno, H., Martin, O., Stützle, T.: Iterated local search. ArXiv Mathematics e-prints. (Feburary 2001), arXiv:math/0102188
  19. 19.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Computers & Operations Research 24(11), 1097–1100 (1997)CrossRefMATHMathSciNetGoogle Scholar
  20. 20.
    Montgomery, D.: Design and Analysis of Experiments, 7th edn. Wiley (2008)Google Scholar
  21. 21.
    Nel, S., Kizil, M., Knights, P.: Improving truck-shovel matching. In: 35TH APCOM Symposium, The Australasian Institute of Mining and Metallurgy, Wollongong, NSW, pp. 381–391 (September 2011)Google Scholar
  22. 22.
    Souza, M., Coelho, I., Ribas, S., Santos, H., Merschmann, L.: A hybrid heuristic algorithm for the open-pit-mining operational planning problem. European Journal of Operational Research 207(2), 1041–1051 (2010)CrossRefMATHGoogle Scholar
  23. 23.
    Subtil, R., Silva, D., Alves, J.: A practical approach to truck dispatch for open pit. In: 35th International Symposium on Application of Computers in the Minerals Industry (35th APCOM) pp. 765–777 (2011)Google Scholar
  24. 24.
    Tan, Y., Chinbat, U., Miwa, K., Takakuwa, S.: Operation modeling and analysis of open pit copper mining using GPS tracking data. In: Proceedings of the 2012 Winter Simulation Conference. pp. 1–12. IEEE, Berlin (2012)Google Scholar
  25. 25.
    Topal, E., Ramazan, S.: A new MIP model for mine equipment scheduling by minimizing maintenance cost. European Journal of Operational Research 207(2), 1065–1071 (2010)CrossRefMATHGoogle Scholar
  26. 26.
    Topal, E., Ramazan, S.: Mining truck scheduling with stochastic maintenance cost. Journal of Coal Science and Engineering (China) 18(3), 313–319 (2012)CrossRefGoogle Scholar
  27. 27.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100. International Center for Numerical Methods in Engineering (CIMNE) (2002)Google Scholar
  28. 28.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rafael Frederico Alexandre
    • 1
    • 2
    • 3
  • Felipe Campelo
    • 1
  • Carlos M. Fonseca
    • 4
  • João Antônio de Vasconcelos
    • 1
    • 2
  1. 1.Graduate Program in Electrical EngineeringFederal University of Minas GeraisBelo HorizonteBrazil
  2. 2.Evolutionary Computation LaboratoryFederal University of Minas GeraisBelo HorizonteBrazil
  3. 3.Department of Computer and SystemsFederal University of Ouro PretoJoão MonlevadeBrazil
  4. 4.Department of Informatics EngineeringUniversity of Coimbra Poló IICoimbraPortugal

Personalised recommendations