2084 Cullenbine, C., Wood, R. K., and Newman, A.A. (2011). A sliding time window heuristic for open pit mine block sequencing. Optimization Letters. 5, 365-377. hiips://doi.org/10.1007/s11590-011-0306-2. Da Mata, J. F. C., Nader, A. S., and Mazzinghy, D. B. (2023) A Case Study of Incorporating Variable Recovery and Specific Energy in Long-Term Open Pit Mining. Mining. 3, 367-386. hiips://doi.org/10.3390/mining3020022. Espinoza, D., Goycoolea, M., Moreno, E., and Newman, A. (2013). MineLib: A library of open pit mining problems. Annals of Operations Research. 206(1), 93-114. GMG (Global Mining Guidelines Group) (2025). Introduction to Geometallurgy. Available from: <hiips://gmggroup.org/introduction-to-geometallurgy-white-paper/>. Hochbaum, D. S. (2001). A new-old algorithm for minimum-cut and maximum-flow in closure graphs. Networks: An International Journal. 37(4), 171-193. hiips://doi.org/10.1002/net.1012. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science. 220(4598), 671-680. hiips://doi.org/10.1126/science.220.4598.67. Lamghari, A., Dimitrakopoulos, R., and Ferland, J. A. (2015). A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines. Journal of Global Optimization. 63, 555-582. hiips://doi.org/10.1007/s10898-10014-10185-z. Lane, K. F. (1964). Choosing the optimum cut-off grade. Quarterly of Colorado School of Mines. 29, 811829. Lerchs, H. and Grossmann, I. F. (1965). Optimum Design of Open Pit Mines. CIM Bulletin. 58, 47-54. Mai, N. L., Topal, E., and Erten, O. (2018). A new open-pit mine planning optimization method using block aggregation and integer programming. Journal of the Southern African Institute of Mining and Metallurgy. 118, 705-714. hiips://doi.org/10.17159/2411-9717/2018/v118n7a4. Martins, S., Campos, P., Mazzinghy, D. (2025). A case study comparing the results of a copper open-pit mine scheduling considering two different approaches for spatial interpolation of comminution geometallurgical variables. Journal of Sustainable Mining. 24, 472-485. hiips://doi.org/10.46873/23003960.1470. Mariz, J. L. V., and Soofastaei, A. (2022). Advanced Analytics for Surface Mine Planning. In: Advanced Analytics in Mining Engineering. Cham, Springer. hiips://doi.org/10.1007/978-3-030-91589-6_9. Mariz, J. L. V., Badiozamani, M. M., Peroni, R. L., and Silva, R. M. A. (2024). A critical review of bench aggregation and mining cut clustering techniques based on optimization and artificial intelligence to enhance the open-pit mine planning. Engineering Applications of Artificial Intelligence. 133, 108334. hiips://doi.org/10.1016/j.engappai.2024.108334. Mazzinghy, D. B.; Varela, N. V. M.; Brickey, A.; Suazo, G. I. N.; Ortiz, J.; and Souza, M. J. F. (2025). Mineral processing plant capacity based on geometallurgical block model scheduling. In: Proceeding of the 42nd International APCOM Symposium, Perth. Meagher, C., Dimitrakopoulos, R., and Avis, D. (2014). Optimized open pit mine design, pushbacks, and the gap problem-A review. Journal of Mining Science. 50(3), 508526. hiips://doi.org/10.1134/S1062739114030132. Minelib (2025). Instance: marvin. Available from: <hiips://mansciweb.uai.cl/minelib/marvin.xhtml>. Morales, N., Jélvez, E., Nancel-Penard, P., Marinho, A., and Guimarães, O. (2015). A Comparison of Conventional and Direct Block Scheduling Methods for Open Pit Mine Production Scheduling. In: Proceeding of the 37th International APCOM Symposium, Alaska. Morales, N., Seguel, S., Cáceres, A., and Jélvez, E. (2019). Alarcón, M. Incorporation of Geometallurgical Attributes and Geological Uncertainty into Long-Term Open-Pit Mine Planning. Minerals. 9, 108. hiips://doi.org/10.3390/min9020108. Moreno, E., Espinoza, D., and Goycoolea, M. (2010). Large-scale multi-period precedence constrained knapsack problem: A mining application. Electronic Notes in Discrete Mathematics. 36, 407-414. hiips://doi.org/10.1016/j.endm.2010.05.052. Niquini, F. G. F. and Costa, J. F. C. L. (2020a). Mass and Metallurgical Balance Forecast for a Zinc Processing Plant Using Artificial Neural Networks. Natural Resources Research, 29, 3569-3580. hiips://doi.org/10.1007/s11053-020-09678-4.
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