Solving Complex Mine Optimisation Problems Using Blend Vectoring and Multi-objective Production Scheduling

  • Daniel HtweEmail author
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)


Developing a large scale open pit mining project constitutes a complex process where decisions made at the planning level can affect the overall project economics and value in the scale of billions of dollars. In this paper, it will be demonstrated how the project reserve, and expected economic value can be increased substantially depending on the ability within both the optimisation and scheduling process in accounting for material process classification based on a blend vectored resource. This will be one of the first examples within the literature where the differences in ability to handle this fundamental concept is quantified as comparative analysis is provided to conventional approaches. These are applied within a practical implementation for a Western Australian iron ore case study. The elementary methodology of traditional cut-off optimisation to determine ‘ore’ is evaluated in comparison to results generated from applying metaheuristic blend vector techniques and derived mixed integer linear programming formulations. It will be shown how generating the results based on these techniques is computationally efficient in practice, with minimal engineering time consumed to generate solutions despite datasets generally being very large. Furthermore, post-schedule level results obtained from conventional scheduling and multi-objective genetic algorithms are provided. These explicitly demonstrate how the overall project value possesses a strong dependence on the mine planning conditions imposed by different approaches. A point of view on the open pit mine production schedule optimisation problem is also given that has allowed the development of high quality solutions in practical instances.


Mixed integer linear programming Iron-ore blending Optimisation Production scheduling Operations research Mining engineering 


  1. 1.
    Bienstock, D., Zuckerberg, M.: A new LP algorithm for precedence constrained production scheduling.
  2. 2.
    Boland, N., Dumitrescu, I., Froyland, G., Gleixner, A.M.: LP-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity. Comput. Oper. Res. 36(4), 1064–1089 (2009)CrossRefGoogle Scholar
  3. 3.
    Chicoisne, R., Espinoza, D., Goycoolea, M., Moreno, E., Rubio, E.: A new algorithm for the open-pit mine production scheduling problem. Oper. Res. 60(3), 517–528 (2012)CrossRefGoogle Scholar
  4. 4.
    Everett, J.E.: Planning an iron ore mine: from exploration data to informed mining decisions. Issues Inform. Sci. Inform. Technol. 10(1), 145–162 (2013)CrossRefGoogle Scholar
  5. 5.
    Goldberg, A., Tarjan, R.: A new approach to the maximum-flow problem. J. Assoc. Comput. Mach. 35(4), 921–940 (1988)CrossRefGoogle Scholar
  6. 6.
    Hochbaum, D.: A new-old algorithm for minimum-cut and maximum-flow in closure graphs. Networks 37(4), 171–193 (2001)CrossRefGoogle Scholar
  7. 7.
    Hochbaum, D., Chen, A.: Performance analysis and best implementations of old and new algorithms for the open-pit mining problem. Oper. Res. 48(6), 894–914 (2000)CrossRefGoogle Scholar
  8. 8.
    Htwe, D., Asad, M.W.A.: Performance evaluation and an implementation of mixed integer linear programming for open pit optimisation. Unpublished Paper (2015).
  9. 9.
    Johnson, T.B.: Optimum open pit mine production scheduling. Doctoral dissertation, University of California, Berkeley, California (1968)Google Scholar
  10. 10.
    Lambert, W.B., Brickey, A., Newman, A.M., Eurek, K.: Open-pit block sequencing formulations: a tutorial. Interfaces 44(2), 127–142 (2014)CrossRefGoogle Scholar
  11. 11.
    Myburgh, C., Deb, K.: Evolutionary algorithms in large-scale open pit mine scheduling. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO 2010), Portland, Oregon, pp. 1155–1162. ACM (2010)Google Scholar
  12. 12.
    Picard, J.C.: Maximal closure of a graph and applications to combinatorial problems. Manag. Sci. 22(11), 1268–1272 (1976)CrossRefGoogle Scholar
  13. 13.
    Puchinger, J., Raidl, G., Pferschy, U.: The multidimensional knapsack problem: structure and algorithms. INFORMS J. Comput. 22(2), 250–265 (2010)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.OrelogyPerthAustralia

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