Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alexandre, R., Vasconcelos, J., Campelo, F.: Additional electronic files. http://cpdee.ufmg.br/~fcampelo/files/MOPMOPP/ (2014)
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)
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)
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)
Coello, C., Lamont, G., Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-Objective Problem, 2nd edn. Springer (2007)
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)
Dias, A., Vasconcelos, J.: Multiobjective genetic algorithms applied to solve optimization problems. IEEE Transactions on Magnetics 38(2), 1133–1136 (2001)
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)
Feo, T., Resende, M.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6(2), 109–133 (1995)
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)
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)
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley (1989)
Hansen, P., Mladenovic, N., Pérez, J.M.: Variable neighbourhood search: methods and applications. 4OR 6(4), 319–360 (2008)
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)
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)
ILOG: Users Manual. IBM (2008)
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)
Loureno, H., Martin, O., Stützle, T.: Iterated local search. ArXiv Mathematics e-prints. (Feburary 2001), arXiv:math/0102188
Mladenovic, N., Hansen, P.: Variable neighborhood search. Computers & Operations Research 24(11), 1097–1100 (1997)
Montgomery, D.: Design and Analysis of Experiments, 7th edn. Wiley (2008)
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)
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)
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)
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)
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)
Topal, E., Ramazan, S.: Mining truck scheduling with stochastic maintenance cost. Journal of Coal Science and Engineering (China) 18(3), 313–319 (2012)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Alexandre, R.F., Campelo, F., Fonseca, C.M., de Vasconcelos, J.A. (2015). A Comparative Study of Algorithms for Solving the Multiobjective Open-Pit Mining Operational Planning Problems. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-15892-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15891-4
Online ISBN: 978-3-319-15892-1
eBook Packages: Computer ScienceComputer Science (R0)