Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera
ilustraciones, diagramas
- Autores:
-
Toro Morales, Diego Alejandro
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84945
- Palabra clave:
- 620 - Ingeniería y operaciones afines::622 - Minería y operaciones relacionadas
Minas de oro
Gold mines and mining
optimización estocástica
ley de corte
Valor Presente Neto
Algoritmos Genéticos
minería subterránea
oro
stochastic optimization
cut-off grade
Net Present Value
Genetic Algorithms
underground mining
gold
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| dc.title.translated.eng.fl_str_mv |
Stochastic optimization model of cut-off grades for a gold mining company |
| title |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| spellingShingle |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera 620 - Ingeniería y operaciones afines::622 - Minería y operaciones relacionadas Minas de oro Gold mines and mining optimización estocástica ley de corte Valor Presente Neto Algoritmos Genéticos minería subterránea oro stochastic optimization cut-off grade Net Present Value Genetic Algorithms underground mining gold |
| title_short |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| title_full |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| title_fullStr |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| title_full_unstemmed |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| title_sort |
Modelo de optimización estocástica de leyes de corte para una compañía minera aurífera |
| dc.creator.fl_str_mv |
Toro Morales, Diego Alejandro |
| dc.contributor.advisor.none.fl_str_mv |
Franco Sepúlveda, Giovanni Del Rio Cuervo, Juan Camilo |
| dc.contributor.author.none.fl_str_mv |
Toro Morales, Diego Alejandro |
| dc.contributor.orcid.spa.fl_str_mv |
Franco Sepúlveda, Giovanni [0000-0003-4579-8389] Del Rio Cuervo, Juan Camilo [0000-0003-0091-354X] |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::622 - Minería y operaciones relacionadas |
| topic |
620 - Ingeniería y operaciones afines::622 - Minería y operaciones relacionadas Minas de oro Gold mines and mining optimización estocástica ley de corte Valor Presente Neto Algoritmos Genéticos minería subterránea oro stochastic optimization cut-off grade Net Present Value Genetic Algorithms underground mining gold |
| dc.subject.lemb.spa.fl_str_mv |
Minas de oro |
| dc.subject.lemb.eng.fl_str_mv |
Gold mines and mining |
| dc.subject.proposal.spa.fl_str_mv |
optimización estocástica ley de corte Valor Presente Neto Algoritmos Genéticos minería subterránea oro |
| dc.subject.proposal.eng.fl_str_mv |
stochastic optimization cut-off grade Net Present Value Genetic Algorithms underground mining gold |
| description |
ilustraciones, diagramas |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-11-14T19:54:48Z |
| dc.date.available.none.fl_str_mv |
2023-11-14T19:54:48Z |
| dc.date.issued.none.fl_str_mv |
2023-11-09 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/84945 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/84945 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
| dc.relation.indexed.spa.fl_str_mv |
RedCol LaReferencia |
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Abdel Sabour, S. A., & Dimitrakopoulos, R. (2011). Incorporating geological and market uncertainties and operational flexibility into open pit mine design. Journal of Mining Science, 47(2), 191–201 Abdolahisharif, J., Bakhtavar, E., & Anemangely, M. (2012). Optimal cut-off grade determination based on variable capacities in open-pit mining. Journal of the South African Institute of Mining and Metallurgy, 112(1065–1069) Ahmadi, M. A., & Golshadi, M. (2012). Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. Journal of Petroleum Science and Engineering, 98–99, 40–49 Ahmadi, M. R. (2018). Cutoff grade optimization based on maximizing net present value using a computer model. Journal of Sustainable Mining, 17(2), 68–75. https://doi.org/10.1016/j.jsm.2018.04.002 Ahmadi, M. R., & Bazzazi, A. A. (2019). Cutoff grades optimization in open pit mines using meta-heuristic algorithms. Resources Policy, 60, 72–82. https://doi.org/10.1016/j.resourpol.2018.12.001 Ahmadi, M. R., & Shahabi, R. S. (2018). Cutoff grade optimization in open pit mines using genetic algorithm. Resources Policy, 55, 184–191. https://doi.org/10.1016/j.resourpol.2017.11.016 Alford, C., & Hall, B. (2009). Stope optimisation tools for selection of optimum cut-off grade in underground mines. Project Evaluation Conference. Arteaga, J. D. (2015). Modelo de optimización estocástica de la ley de corte para depósitos polimetálicos. Universidad Nacional de Colombia. Asad, M. W. A. (2002). Development of generalized cuttoff grade optimization algorithm for open pit mining operations. Journal of Engineering and Applied Sciences, 21(2), 119–127. Asad, M. W. A. (2005a). Cut-off grade optimization algorithm with stockpiling option for open pit mining operations of two economic minerals. International Journal of Surface Mining, Reclamation and Environment, 19(3), 176–187. Asad, M. W. A. (2007). Optimum cut-off grade policy for open pit mining operations through net present value algorithm considering metal price and cost escalation. Engineering Computations, 24(7), 723–736. Asad, M. W. A. (2005b). Cut-off grade optimization algorithm for open pit mining operations with consideration of dynamic metal price and cost escalation during mine life. 32nd International Symposium on Application of Computers and Operations Research in the Mineral Industry, 273–277. Asad, M. W. A., & Dimitrakopoulos, R. (2013). A heuristic approach to stochastic cut-off grade optimization for open pit mining complexes with multiple processing streams. Resources Policy, 38(4), 591–597. https://doi.org/10.1016/j.resourpol.2013.09.008 Asad, M. W. A., Qureshi, M. A., & Jang, H. (2016). A review of cut-off grade policy models for open pit mining operations. Resources Policy, 49, 142–152. https://doi.org/10.1016/j.resourpol.2016.05.005 Asad, M. W. A., & Topal, E. (2011). Net present value maximization model for optimum cut-off grade policy of open pit mining operations. Journal of the Southern African Institute of Mining and Metallurgy, 111(11), 741–750. Ataei, M., & Osanloo, M. (2003a). Determination of optimum cut-off grades of multiple metal deposits by using the golden section search method. Journal of the South African Institute of Mining and Metallurgy, 493–500. Ataei, M., & Osanloo, M. (2003b). Methods for calculation of optimal cutoff grades in complex ore deposits. Journal of Mining Science, 39(5), 499–507. Ataei, M., & Osanloo, M. (2004). Using a combination of genetic algorithm and the grid search method to determine optimum cut-off grades of multiple metal deposits. International Journal of Surface Mining, Reclamation and Environment, 18(1), 60–78. https://doi.org/10.1076/ijsm.18.1.60.23543 Ataei, M., & Osanloo, M. (2013). Determination of optimum cut-off grades of open pit mines with the purpose of maximizing net present value using elimination methods. International Journal of Engineering Science, 14(3), 141–151. Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2007, 4661–4667. Azimi, Y., & Osanloo, M. (2011). Determination of open pit mining cut-off grade strategy using combination of nonlinear programming and genetic algorithm. Archives of Mining Sciences, 56(2), 189–212. Azimi, Y., Osanloo, M., & Esfahanipour, A. (2011). Optimisation of mining policy under different economic conditions using a combination of non-linear programing and genetic algorithm. Proceedings of the 35th APCOM Symposium. Azimi, Y., Osanloo, M., & Esfahanipour, A. (2012). Selection of the open pit mining cut-off grade strategy under price uncertainty using a risk based multi-criteria ranking system. Archives of Mining Sciences, 57(3), 741–768. Azimi, Y., Osanloo, M., & Esfahanipour, A. (2013). An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection. Resources Policy, 38(2), 212–223. https://doi.org/10.1016/j.resourpol.2013.01.004 Baker, C. K., & Giacomo, S. M. (1998). Resource and reserves: their uses and abuses by the equity markets. Ore Reserves and Finance: A Joint Seminar between Australasian Institute of Mining and Metallurgy and ASX. Barr, D. (2012). Stochastic Dynamic Optimization of Cut-off Grade in Open Pit Mines. Queen’s University. Bascetin, A., & Nieto, A. (2007). Determination of optimal cut-off grade policy to optimize NPV using a new approach with optimization factor. Journal of the South African Institute of Mining and Metallurgy, 107(2), 87–94. Batterham, R., & Elvish, R. (2009). Smarter mineral processing, or, what do mill operators think? 10th Mill Operators’ Conference Proceedings 2009–1. https://app.knovel.com/hotlink/pdf/id:kt008V7C34/tenthmill- operators/smart%0Aer-mineral-abstract Birch, C. (2016). Impact of discount rates on cut-off grades for narrow tabular gold deposits. Journal of the Southern African Institute of Mining and Metallurgy, 116(2), 115–122. https://doi.org/10.17159/2411-9717/2016/v116n2a2 Birch, C. (2018). Review of cut-off grade optimisation from Southern African mines. Student assignment based observations. Resources Policy, 56, 134–140. https://doi.org/10.1016/j.resourpol.2017.10.004 Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654. Bootsma, M. T., Alford, C., Bennford, J., & Buxton, M. W. N. (2018). Cut-off grade based sublevel stope mine optimisation. In R. Dimitrakopoulos (Ed.), Advances in Applied Strategic Mine Planning (1st ed., pp. 537–558). Springer. Border, S. (1991). Optimisation of cut-off grades during design of underground mines. Mining Industry Optimisation Conference. Boyle, P. (1997). Options: a Monte Carlo approach. Journal of Financial Economics, 4, 323–338. Bragin, V. I., Kharitonova, M. Y., & Matsko, N. A. (2021). A probabilistic approach to the dynamic cut-off grade assessment. Journal of Mining Institute, 251(3), 617–625. https://doi.org/10.31897/PMI.2021.5.1 Cepin, M. (2011). Assessment of power system reliability. Springer-Verlag London. https://doi.org/10.1007/978-0-85729-688-7 Cetin, E., & Dowd, P. A. (2002). The use of genetic algorithms for multiple cut-off grade optimization. In Proceedings of the 23rd International Symposium on Application of Computers and Operations Research in Minerals Industry (pp. 769–779). Cetin, E., & Dowd, P. A. (2016). Multiple cut-off grade optimization by genetic algorithms and comparison with grid search method and dynamic programming. Journal of the South African Institute of Mining and Metallurgy, 116, 681–688. https://doi.org/https://doi.org/10.17159/2411-9717/2016/ v116n7a10. Chimunhu, P., Topal, E., Ajak, A. D., & Asad, W. (2022). A review of machine learning applications for underground mine planning and scheduling. Resources Policy, 77, 102693. https://doi.org/10.1016/j.resourpol.2022.102693 Cox, J. C., Ross, S. A., & Rubinstein, M. (1979). Option pricing: a simplified approach. Journal of Financial Economics, 7(3), 229–263. Dagdelen, K. (1992). Cut-off grade optimization. In Proceedings of the 23rd International Symposium on Application of Computers and Operations Research in Minerals Industry (pp. 157–165). Dagdelen, K. (1993). An NPV optimization algorithm for open pit mine design. In Proceedings of the 24th International Symposium on Application of Computers and Operations Research in Minerals Industry (pp. 257–263). Dagdelen, K., & Kawahata, K. (2008). Value creation through strategic mine planning and cut-off grade optimization. Mining Engineering, 60(1), 39–45. Dagdelen, K., & Kawahata, K. (2007). Cut-off grade optimization for large scale multi-mine, multi process mining operations. In C. Associates (Ed.), Proceedings of the International Symposium on Mine Planning and Equipment Selection (pp. 226–233). Dagdelen, K., & Mohammad, W. A. (1997). Multi-mineral cut-off grade optimization with option to stockpile. SME Annual Meeting, Preprint #97186. Dimitrakopoulos, R. (2018). Advances in Applied Strategic Mine Planning (1st ed.). https://doi.org/10.1007/978-3-319-69320-0 Dong, C. H. (2002). Application of Ore Grade Optimization Method on Erfengshan Iron Mine. Metal Mine, 4, 14–17. Dullaert, W., Sevaux, M., Sörensen, K., & Springael, J. (2007). Applications of metaheuristics. European Journal of Operational Research, 179, 601–604. https://doi.org/10.1016/j.ejor.2005.03.060 Fan, J., Xiong, S., Wang, J., & Gong, C. (2008). IMODE: Improving multi-objective differential evolution algorithm. Proceedings of the Fourth International Conference on Natural Computation, ICNC’08, 212–216. Fathollahzadeh, K., Asad, M. W. A., Mardaneh, E., & Cigla, M. (2021). Review of solution methodologies for open pit mine production scheduling problem. International Journal of Mining Reclamation and Environment, 35(8), 564–599. Franco-Sepúlveda, G., Del Rio-Cuervo, J. C., & Pachón-Hernández, M. A. (2019). State of the art about metaheuristics and artificial neural networks applied to open pit mining. Resources Policy, 60, 125–133. https://doi.org/10.1016/j.resourpol.2018.12.013 Franco-Sepúlveda, G., & Velilla-Avilez, D. (2014). Planeamiento minero como función de la variación de la ley de corte crítica. Boletín Ciencias de La Tierra, 35, 25–30. https://www.redalyc.org/articulo.oa?id=169531421003 Franco, G. (2017). Modelo de optimización estocástica para explotaciones mineras a cielo abierto. Universidad Nacional de Colombia. Franks, M. D., Boger, D. V., Côte, C. M., & Mulligan, D. . (2011). Sustainable development principles for the disposal of mining and mineral processing wastes. Resources Policy, 36(2), 114–122. Gholamnejad, J. (2008). Determination of the optimum cut-off grade considering environmental cost. Journal of International Environmental Application and Science, 3(3), 186–194. Githiria, J., & Musingwini, C. (2019). A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value. Journal of the Southern African Institute of Mining and Metallurgy, 119(3), 217–228. https://doi.org/10.17159/2411-9717/2019/v119n3a1 Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning (1st ed.). Addison-Wesley Longman Publishing Co., Inc. Gómez, E. A., & Díez, J. M. (2015). Evaluación financiera de proyectos (Segunda Ed). Gonzalez, T. (2007). Handbook of Approximation Algorithms and Metaheuristics. Chapman & Hall/CRC. Goodfellow, R. C., & Dimitrakopoulos, R. (2016). Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing, 40, 292–304. Gu, X., Wang, Q., Chu, D., & Zhang, B. (2010). Dynamic optimization of cutoff grade in underground metal mining. 17, 492–497. https://doi.org/10.1007/s11771 Gupta, J. N. D., & Sexton, R. S. (1999). Comparing backpropagation with a genetic algorithm for neural network training. Omega, 27(6), 679–684. https://doi.org/10.1016/S0305-0483(99)00027-4 Hajkowicz, A. S., Heyenga, S., & Moffat, K. (2011). The relationship between mining and socio-economic well being in Australia’s regions. Resources Policy, 36(1), 30–38. Hall, B. (2014). Cut-off Grades and Optimising the Strategic Mine Plan. The Australasian Institute of Mining and Metallurgy. Hanafi, S., Wang, Y., Glover, F., Yang, W., & Hennig, R. (2023). Tabu search exploiting local optimality in binary optimization. European Journal of Operational Research, 308, 1037–1055. https://doi.org/10.1016/j.ejor.2023.01.001 Hassan, R., Cohanim, B., & Weck, O. (2005). A comparison of particle swarm optimization and genetic algorithm. Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, 18–21. He, Y., Xu, S., Zhu, K., Liu, T., & Li, Y. (2008). A Genetic-Neural Method of Optimizing Cut-Off Grade. In Advances in Neural Networks - ISNN 2008 (pp. 588–597). Springer, Berlin, Heidelberg. He, Y., Zhu, K., Gao, S., Liu, T., & Li, Y. (2009). Theory and method of genetic - neural optimization cut-off grade and grade of crude ore. Expert Systems with Applications, 36(4), 7617–7623. Hirai, H., Katamura, K., Mamaclay, F. P., & Fujimura, T. (1987). Development and Mine Operation at Rio Tuba Nickel Mine. International Journal of Mineral Processing, 19, 99–114. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. The University of Michigan Press/MIT Press. Hosseini-Nasab, E., Khezri, M., Khodamoradi, M. S., & Atashpaz-Gargari, E. (2010). An application of imperialist competitive algorithm to simulation of energy demand based on economic indicators: evidence from Iran. European Journal of Scientific Research, 43(4), 495–506. Izmailov, A. F., & Solodov, M. V. (2013). A globally convergent algorithm for convex programming problems with binary variables. Mathematical Programming, 142(1–2), 233–258. Jöhnk, J., Weißert, M., & Wyrtki, K. (2020). Ready or not, AI comes - an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(5), 20. https://doi.org/https://doi.org/ 10.1007/s12599-020-00676-7 Khan, A., & Asad, M. W. A. (2019). A method for optimal cut-off grade policy in open pit mining operations under uncertain supply. Resources Policy, 60, 178–184. https://doi.org/10.1016/j.resourpol.2018.12.003 Khan, A., & Asad, M. W. A. (2021). A mixed integer programming based cut-off grade model for open-pit mining of complex poly-metallic resources. Resources Policy, 72, 1–9. https://doi.org/10.1016/j.resourpol.2021.102076 Khodayari, A. A., & Jafarnejad, A. (2012). The effect of price changes on optimum cut-off grade of different open-pit mines. Journal of Mining & Environment, 3(1), 61–68. Khodayari, A., & Jafarnejad, A. (2012). Cut-off grade optimization for maximizing the output rate. International Journal of Mining and Geo-Engineering, 46(1), 51–56. King, B. (2001). Optimal Mine Scheduling Policies. London University, UK. King, B. (2011). Optimal mining practice in strategic planning. Journal of Mining Science, 47(2), 247–253. King, B. (2018). Optimal Mining Principles. In R. Dimitrakopoulos (Ed.), Advances in Applied Strategic Mine Planning (1st ed., pp. 19–30). Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. Kumral, M. (2013). Optimizing ore-waste discrimination and block sequencing through simulated annealing. Applied Soft Computing, 13(8), 3737–3744. Lane, K. F. (1964). Choosing the optimum cut-off grade. Colorado School of Mines Quarterly, 59, 811–829. Lane, K. F. (1988). The economic definition of ore: Cut-off grade in theory and practice. Mining Journal Books. Laurence, D. (2011). A Guide to Leading Practice Sustainable Development in Mining. Australian Government Department of Resources, Energy and Tourism. Li, S., & Yang, C. (2012). An optimal algorithm for cut-off grade calculation using multistage stochastic programming. Journal of Theoretical and Applied Information Technology, 45(1), 117–122. Liu, D., Li, G., Hu, N., Xiu, G., & Ma, Z. (2019). Optimization of the cut-off grade for underground polymetallic mines. Gospodarka Surowcami Mineralnymi / Mineral Resources Management, 35(1), 25–42. https://doi.org/10.24425/gsm.2019.128198 Mahase, M., Musingwini, C., & Nhleko, A. (2016). A survey of applications of multi-criteria decision analysis methods in mine planning and related case studies. Journal of the Southern African Institute of Mining and Metallurgy, 11, 1051–1056. Maldonado, C. E., & Gómez, N. A. (2011). El mundo de las ciencias de la complejidad. Universidad El Rosario. Malik, H., Iqbal, A., Joshi, P., Agrawal, S., & Bakhsh, F. (2021). Metaheuristic and Evolutionary Computation: Algorithms and Applications (Springer (ed.); 1st ed.). Mansouri, M., Osanloo, M., & Gheisari, N. (2014). Establishing a Sustainable Model to Reduce the Risk of Mine Closure. Mine Planning and Equipment Selection, 1427–1436. https://doi.org/http: //dx.doi.org/10.1007/978-3-319-02678-7_137 Marques, D. M., & Costa, J. F. C. L. (2013). An algorithm to simulate ore grade variability in blending and homogenization piles. International Journal of Mineral Processing, 120, 48–55. Martinelli, R., Collard, J., & Gamache, M. (2020). Strategic planning of an underground mine with variable cut-off grades. Optimization and Engineering, 21(3), 803–849. https://doi.org/10.1007/s11081-019-09479-6 McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 15–33. McInerney, M., & Dhawan, A. P. (1993). Use of genetic algorithms with backpropagation in training of feedforward neural networks. Proceedings of the IEEE International Conference on Neural Networks, 203–208. Melián, B., Moreno, J. A., & Moreno, J. M. (2003). Metaheurísticas: Una visión global. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 7(19), 7–28. Michalewicz, Z., & Fogel, D. B. (2004). How to Solve It: Modern Heuristics (2nd ed.). Springer Berlin, Heidelberg. https://doi.org/https://doi.org/10.1007/978-3-662-07807-5 Minnitt, R. C. A. (2003). Cut-off grade determination for the maximum value of a small Wits-type gold mining operation. Proceedings of the 31st International Symposium on Application of Computers and Operations Research in the Minerals Industries (APCOM). Mishra, B. (2006). Development of a Computer Model for Determination of Cut off Grade for Metalliferous Deposits. Journal of Mines, Metals and Fuels, 54, 147–152. Mohammad, W. A. (1997). Multi-mineral cut-off grade optimization with option to stockpile. Colorado School of Mines. Mohammadi, S., Ataei, M., Kakaie, R., & Pourzamani, E. (2015). Comparison of golden section search method and imperialist competitive algorithm for optimization cut-off grade - case study: Mine No. 1 of Golgohar. Journal of Mining & Environment, 6, 63–71. Mohammadi, S., Kakaie, R., Ataei, M., & Pourzamani, E. (2017). Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm ( ICA ). Resources Policy, 51(July 2016), 39–48. https://doi.org/10.1016/j.resourpol.2016.11.005 Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. The MIT Press. Mutti, D., Yakovleva, N., Vazquez-Brust, D., & Di Marco, H. M. (2012). Corporate social responsibility in the mining industry: Perspectives from stakeholder groups in Argentina. Resources Policy, 37(2), 212–222. Myburgh, C. A., Deb, K., & Craig, S. (2014). Applying Modern Heuristics to Maximizing NPV through Cutoff grade Optimization. Orebody Modelling and Strategic Planning Conference, 1–16. Narendran, T. V., & Weinelt, B. (2017). Digital transformation initiative mining and metals industry. Newman, A. M., Rubio, E., Caro, R., Weintraub, A., & Eurek, K. (2010). A review of operations research in mine planning. Interfaces, 40(3), 222–245. https://doi.org/10.1287/inte.1090.0492 Niemann-Delius, C., & Sattarvand, J. (2008). Perspective of metaheuristic optimization methods in open pit production planning. Mineral Resources Management = Gospodarka Surowcami Mineralnymi, 24(4,2), 143–155. Noriega, R., & Pourrahimian, Y. (2022). A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning. Resources Policy, 77, 102727. https://doi.org/10.1016/j.resourpol.2022.102727 Oliva-Romero, Y., Ochoa-Zezatti, A., Marcela-Herrera, A., & Oliva-Navarro, D. A. (2017). Modelo innovador para un aparador comercial usando un algoritmo competitivo imperialista. Research in Computing Science, 134(1), 35–44. https://doi.org/10.13053/rcs-134-1-3 Osanloo, M., Ataei, M. (2003). Using Equivalent Grade Factors to Find the Optimum Cut-off Grades of Multiple Metal Deposits. Minerals Engineering, 16, 771–776. Osanloo, M., Rashidinejad, F., & Rezai, B. (2008). Incorporating environmental issues into optimum cut-off grades modelling at porphyry copper deposits. Resources Policy, 33(4), 222–229. Paithankar, A., Chatterjee, S., & Goodfellow, R. (2021). Open-pit mining complex optimization under uncertainty with integrated cut-off grade based destination policies. Resources Policy, 70, 101875. https://doi.org/10.1016/j.resourpol.2020.101875 Prior, T., Giurco, D., Mudd, G., Mason, L., & Behrisch, J. (2012). Resource depletion, peak minerals and the implications for sustainable resource management. Global Environmental Change, 22(3), 577–587. https://doi.org/https://doi.org/10.1016/j.gloenvcha.2011.08.009. Rafiee, R., Ataei, M., & Azarfar, A. (2016). Determination of optimal open-pit mines with the goal of maximizing net present value using colonial competition algorithm. Journal of Analytical and Numerical Methods in Mining Engineering, 11, 89–99. Rahimi, E., & Ghasemzadeh, H. (2015). A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects. Resources Policy, 46(1), 51–63. Rashidinejad, F., Osanloo, M., & Rezai, B. (2008). Cutoff grades optimization with environmental management: a case study: Sungun copper project, IUST. International Journal of Engineering Science, 19, 1–13. Rendu, J.-M. (2014). An introduction to cut-off grade estimation. Society for Mining, Metallurgy, and Exploration (SME). Rule, C. M., Fouchee, R. J., & Swart, W. C. E. (2015). Run of mine ore upgrading–proof of concept plant for XRF ore sorting. Proceedings of the 6th International Conference on Semi-Autogenous and High Pressure Grinding Technology. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (Pearson (ed.); 4th ed.). Schwartz, E. S. (1977). The valuation of warrants: Implementing a new approach. Journal of Financial Economics, 4(1–2), 79–93. Sexton, R. S., Dorsey, R. E., & Johnson, J. D. (1998). Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decision Support Systems, 22(2), 171–185. Sganzerla, C., Seixas, C., & Conti, A. (2016). Disruptive innovation in digital mining. Procedia Engineering, 138, 64–71. Shinkuma, T., & Nishiyama, T. (2000). The grade selection rule of the metal mines; an empirical study on copper mines. Resources Policy, 26(1), 31–38. Sotoudeh, F., Nehring, M., Kizil, M., Knights, P., & Mousavi, A. (2021). A novel cut-off grade method for increasing the sustainability of underground metalliferous mining operations. Minerals Engineering, 172, 107168. https://doi.org/10.1016/j.mineng.2021.107168 Tahmasebi, P., & Hezarkhani, A. (2009). Application of Optimized Neural Network by Genetic Algorithm. In IAMG09. Stanford University. Taylor, H. K. (1972). General background theory of cut-off grades. In Transactions of the Institution of Mining and Metallurgy (pp. A160–A179). Thompson, M., & Barr, D. (2014). Cut-off grade: A real options analysis. Resources Policy, 42, 83–92. https://doi.org/10.1016/j.resourpol.2014.10.003 Topp, V., Soames, L., Parham, D., & Bloch, H. (2008). Productivity in the Mining Industry: Measurement and Interpretation. Turner-Saad. (2011). A cut-off of liberated and selected ore minerals optimisation based on the geometallurgy concept. Proceedings of the First AUSIMM International Geometallurgy Conference. Uqaili, A. M., & Harijan, K. (2012). Energy, Environment and Sustainable Development (1st ed.). Springer Vienna. https://doi.org/https://doi.org/10.1007/978-3-7091-0109-4 Vallee, M. (2000). Mineral resource + engineering, economic and legal feasibility = ore reserve. CIM Bulletin, 93, 53–61. Wheeler, A. J., & Rodrigues, R. L. (2002). Cutoff-grade analysis at Fazenda Brasileiro: Mine planning for declining gold prices. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, 111(1), 35–46. Xie, Y. L. (1998). Optimization of Cut-off Grade in Open-pit Based on Control Theory. Transactions of Nonferrous Metals Society of China, 8, 353–356. Yang, X. S. (2013). Optimization and Metaheuristic Algorithms in Engineering. Metaheuristics in Water, Geotechnical and Transport Engineering, 1–23. https://doi.org/10.1016/B978-0-12-398296-4.00001-5 Young, A., & Rogers, P. (2019). A review of digital transformation in mining. Mining, Metallurgy & Exploration, 36, 683–699. |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Franco Sepúlveda, Giovanni8fa3f0b6d38e1f9e02674136a820e381Del Rio Cuervo, Juan Camilo4a94ca842a25e86396205ab9083877b1Toro Morales, Diego Alejandro48be3b6c212aa74891f374bd9a11dd10Franco Sepúlveda, Giovanni [0000-0003-4579-8389]Del Rio Cuervo, Juan Camilo [0000-0003-0091-354X]2023-11-14T19:54:48Z2023-11-14T19:54:48Z2023-11-09https://repositorio.unal.edu.co/handle/unal/84945Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasUna de las variables de decisión más estudiada en la bibliografía técnica minera en relación con su estimación y optimización es la ley de corte, en la que la función objetivo más aceptada ha sido la maximización del Valor Presente Neto (VPN). Sin embargo, un número considerable de proyectos mineros determinan sus leyes de corte a través del uso de modelos determinísticos que no permiten realizar un análisis basado en la incertidumbre. En el presente trabajo se formula un modelo de optimización estocástica de leyes de corte para un depósito aurífero, considerando los riesgos e incertidumbres propias de la actividad minera, con el propósito de maximizar el VPN del proyecto de una compañía con operaciones mineras subterráneas. La metodología seleccionada para el modelo corresponde a la optimización estocástica implícita, que utiliza un enfoque híbrido el cual combina un algoritmo metaheurístico (Algoritmo Genético) y la simulación de Montecarlo. La validación del modelo se realizó utilizando datos reales para verificar su aplicabilidad industrial y proporcionar una alternativa a los modelos tradicionales comúnmente utilizados hasta la fecha. El modelo formulado presentó una vida más corta del proyecto y una ley de corte dinámica en el tiempo, lo que se traduce en ingresos anuales variables. En cuanto a rentabilidad, se presentó un incremento de 21,142,372 USD al comparar la media del VPN del modelo estocástico con el VPN del modelo determinístico. Los resultados obtenidos demuestran los beneficios de aplicar este tipo de modelos a escala industrial para aumentar el valor de los proyectos. (Texto tomado de la fuente]One of the most studied decision variables in the technical mining literature regarding its estimation and optimization is the cut-off grade, where the most accepted objective function has been the maximization of NPV (Net Present Value). However, a considerable number of mining projects determine their cut-off grades using deterministic models that do not facilitate analysis based on uncertainty. In this study, a stochastic optimization model for cut-off grades is formulated for a gold deposit, taking into account the risks and uncertainties inherent in mining activities, with the purpose of maximizing the project's NPV for a company with underground mining operations. The selected methodology for the model is implicit stochastic optimization, employing a hybrid approach that combines a metaheuristic algorithm (Genetic Algorithm) and Monte Carlo simulation. The model's validation is conducted using real data to verify its industrial applicability and to offer an alternative to the commonly employed traditional models. The formulated model exhibits a shorter project life and a dynamic cut-off grade over time, resulting in variable annual revenues. Regarding profitability, a 21,142,372 USD increase is observed when comparing the mean NPV of the stochastic model with that of the deterministic model. These findings demonstrate the advantages of applying such models on an industrial scale to enhance project value.MaestríaMagíster en Ingeniería – Recursos MineralesPlaneamiento minero estocástico y optimización mineraÁrea Curricular de Recursos Mineralesxiv, 100 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Recursos MineralesFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::622 - Minería y operaciones relacionadasMinas de oroGold mines and miningoptimización estocásticaley de corteValor Presente NetoAlgoritmos Genéticosminería subterráneaorostochastic optimizationcut-off gradeNet Present ValueGenetic Algorithmsunderground mininggoldModelo de optimización estocástica de leyes de corte para una compañía minera auríferaStochastic optimization model of cut-off grades for a gold mining companyTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaAbdel Sabour, S. A., & Dimitrakopoulos, R. (2011). Incorporating geological and market uncertainties and operational flexibility into open pit mine design. Journal of Mining Science, 47(2), 191–201Abdolahisharif, J., Bakhtavar, E., & Anemangely, M. (2012). Optimal cut-off grade determination based on variable capacities in open-pit mining. Journal of the South African Institute of Mining and Metallurgy, 112(1065–1069)Ahmadi, M. A., & Golshadi, M. (2012). Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. Journal of Petroleum Science and Engineering, 98–99, 40–49Ahmadi, M. R. (2018). Cutoff grade optimization based on maximizing net present value using a computer model. Journal of Sustainable Mining, 17(2), 68–75. https://doi.org/10.1016/j.jsm.2018.04.002Ahmadi, M. R., & Bazzazi, A. A. (2019). Cutoff grades optimization in open pit mines using meta-heuristic algorithms. Resources Policy, 60, 72–82. https://doi.org/10.1016/j.resourpol.2018.12.001Ahmadi, M. R., & Shahabi, R. S. (2018). Cutoff grade optimization in open pit mines using genetic algorithm. Resources Policy, 55, 184–191. https://doi.org/10.1016/j.resourpol.2017.11.016Alford, C., & Hall, B. (2009). Stope optimisation tools for selection of optimum cut-off grade in underground mines. Project Evaluation Conference.Arteaga, J. D. (2015). Modelo de optimización estocástica de la ley de corte para depósitos polimetálicos. Universidad Nacional de Colombia.Asad, M. W. A. (2002). Development of generalized cuttoff grade optimization algorithm for open pit mining operations. Journal of Engineering and Applied Sciences, 21(2), 119–127.Asad, M. W. A. (2005a). Cut-off grade optimization algorithm with stockpiling option for open pit mining operations of two economic minerals. International Journal of Surface Mining, Reclamation and Environment, 19(3), 176–187.Asad, M. W. A. (2007). Optimum cut-off grade policy for open pit mining operations through net present value algorithm considering metal price and cost escalation. Engineering Computations, 24(7), 723–736.Asad, M. W. A. (2005b). Cut-off grade optimization algorithm for open pit mining operations with consideration of dynamic metal price and cost escalation during mine life. 32nd International Symposium on Application of Computers and Operations Research in the Mineral Industry, 273–277.Asad, M. W. A., & Dimitrakopoulos, R. (2013). A heuristic approach to stochastic cut-off grade optimization for open pit mining complexes with multiple processing streams. Resources Policy, 38(4), 591–597. https://doi.org/10.1016/j.resourpol.2013.09.008Asad, M. W. A., Qureshi, M. A., & Jang, H. (2016). A review of cut-off grade policy models for open pit mining operations. Resources Policy, 49, 142–152. https://doi.org/10.1016/j.resourpol.2016.05.005Asad, M. W. A., & Topal, E. (2011). Net present value maximization model for optimum cut-off grade policy of open pit mining operations. Journal of the Southern African Institute of Mining and Metallurgy, 111(11), 741–750.Ataei, M., & Osanloo, M. (2003a). Determination of optimum cut-off grades of multiple metal deposits by using the golden section search method. Journal of the South African Institute of Mining and Metallurgy, 493–500.Ataei, M., & Osanloo, M. (2003b). Methods for calculation of optimal cutoff grades in complex ore deposits. Journal of Mining Science, 39(5), 499–507.Ataei, M., & Osanloo, M. (2004). Using a combination of genetic algorithm and the grid search method to determine optimum cut-off grades of multiple metal deposits. International Journal of Surface Mining, Reclamation and Environment, 18(1), 60–78. https://doi.org/10.1076/ijsm.18.1.60.23543Ataei, M., & Osanloo, M. (2013). Determination of optimum cut-off grades of open pit mines with the purpose of maximizing net present value using elimination methods. International Journal of Engineering Science, 14(3), 141–151.Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2007, 4661–4667.Azimi, Y., & Osanloo, M. (2011). Determination of open pit mining cut-off grade strategy using combination of nonlinear programming and genetic algorithm. Archives of Mining Sciences, 56(2), 189–212.Azimi, Y., Osanloo, M., & Esfahanipour, A. (2011). Optimisation of mining policy under different economic conditions using a combination of non-linear programing and genetic algorithm. Proceedings of the 35th APCOM Symposium.Azimi, Y., Osanloo, M., & Esfahanipour, A. (2012). Selection of the open pit mining cut-off grade strategy under price uncertainty using a risk based multi-criteria ranking system. Archives of Mining Sciences, 57(3), 741–768.Azimi, Y., Osanloo, M., & Esfahanipour, A. (2013). An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection. Resources Policy, 38(2), 212–223. https://doi.org/10.1016/j.resourpol.2013.01.004Baker, C. K., & Giacomo, S. M. (1998). Resource and reserves: their uses and abuses by the equity markets. Ore Reserves and Finance: A Joint Seminar between Australasian Institute of Mining and Metallurgy and ASX.Barr, D. (2012). Stochastic Dynamic Optimization of Cut-off Grade in Open Pit Mines. Queen’s University.Bascetin, A., & Nieto, A. (2007). Determination of optimal cut-off grade policy to optimize NPV using a new approach with optimization factor. Journal of the South African Institute of Mining and Metallurgy, 107(2), 87–94.Batterham, R., & Elvish, R. (2009). Smarter mineral processing, or, what do mill operators think? 10th Mill Operators’ Conference Proceedings 2009–1. https://app.knovel.com/hotlink/pdf/id:kt008V7C34/tenthmill- operators/smart%0Aer-mineral-abstractBirch, C. (2016). Impact of discount rates on cut-off grades for narrow tabular gold deposits. Journal of the Southern African Institute of Mining and Metallurgy, 116(2), 115–122. https://doi.org/10.17159/2411-9717/2016/v116n2a2Birch, C. (2018). Review of cut-off grade optimisation from Southern African mines. Student assignment based observations. Resources Policy, 56, 134–140. https://doi.org/10.1016/j.resourpol.2017.10.004Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.Bootsma, M. T., Alford, C., Bennford, J., & Buxton, M. W. N. (2018). Cut-off grade based sublevel stope mine optimisation. In R. Dimitrakopoulos (Ed.), Advances in Applied Strategic Mine Planning (1st ed., pp. 537–558). Springer.Border, S. (1991). Optimisation of cut-off grades during design of underground mines. Mining Industry Optimisation Conference.Boyle, P. (1997). Options: a Monte Carlo approach. Journal of Financial Economics, 4, 323–338.Bragin, V. I., Kharitonova, M. Y., & Matsko, N. A. (2021). A probabilistic approach to the dynamic cut-off grade assessment. Journal of Mining Institute, 251(3), 617–625. https://doi.org/10.31897/PMI.2021.5.1Cepin, M. (2011). Assessment of power system reliability. Springer-Verlag London. https://doi.org/10.1007/978-0-85729-688-7Cetin, E., & Dowd, P. A. (2002). The use of genetic algorithms for multiple cut-off grade optimization. In Proceedings of the 23rd International Symposium on Application of Computers and Operations Research in Minerals Industry (pp. 769–779).Cetin, E., & Dowd, P. A. (2016). Multiple cut-off grade optimization by genetic algorithms and comparison with grid search method and dynamic programming. Journal of the South African Institute of Mining and Metallurgy, 116, 681–688. https://doi.org/https://doi.org/10.17159/2411-9717/2016/ v116n7a10.Chimunhu, P., Topal, E., Ajak, A. D., & Asad, W. (2022). A review of machine learning applications for underground mine planning and scheduling. Resources Policy, 77, 102693. https://doi.org/10.1016/j.resourpol.2022.102693Cox, J. C., Ross, S. A., & Rubinstein, M. (1979). Option pricing: a simplified approach. Journal of Financial Economics, 7(3), 229–263.Dagdelen, K. (1992). Cut-off grade optimization. In Proceedings of the 23rd International Symposium on Application of Computers and Operations Research in Minerals Industry (pp. 157–165).Dagdelen, K. (1993). An NPV optimization algorithm for open pit mine design. In Proceedings of the 24th International Symposium on Application of Computers and Operations Research in Minerals Industry (pp. 257–263).Dagdelen, K., & Kawahata, K. (2008). Value creation through strategic mine planning and cut-off grade optimization. Mining Engineering, 60(1), 39–45.Dagdelen, K., & Kawahata, K. (2007). Cut-off grade optimization for large scale multi-mine, multi process mining operations. In C. Associates (Ed.), Proceedings of the International Symposium on Mine Planning and Equipment Selection (pp. 226–233).Dagdelen, K., & Mohammad, W. A. (1997). Multi-mineral cut-off grade optimization with option to stockpile. SME Annual Meeting, Preprint #97186.Dimitrakopoulos, R. (2018). Advances in Applied Strategic Mine Planning (1st ed.). https://doi.org/10.1007/978-3-319-69320-0Dong, C. H. (2002). Application of Ore Grade Optimization Method on Erfengshan Iron Mine. Metal Mine, 4, 14–17.Dullaert, W., Sevaux, M., Sörensen, K., & Springael, J. (2007). Applications of metaheuristics. European Journal of Operational Research, 179, 601–604. https://doi.org/10.1016/j.ejor.2005.03.060Fan, J., Xiong, S., Wang, J., & Gong, C. (2008). IMODE: Improving multi-objective differential evolution algorithm. Proceedings of the Fourth International Conference on Natural Computation, ICNC’08, 212–216.Fathollahzadeh, K., Asad, M. W. A., Mardaneh, E., & Cigla, M. (2021). Review of solution methodologies for open pit mine production scheduling problem. International Journal of Mining Reclamation and Environment, 35(8), 564–599.Franco-Sepúlveda, G., Del Rio-Cuervo, J. C., & Pachón-Hernández, M. A. (2019). State of the art about metaheuristics and artificial neural networks applied to open pit mining. Resources Policy, 60, 125–133. https://doi.org/10.1016/j.resourpol.2018.12.013Franco-Sepúlveda, G., & Velilla-Avilez, D. (2014). Planeamiento minero como función de la variación de la ley de corte crítica. Boletín Ciencias de La Tierra, 35, 25–30. https://www.redalyc.org/articulo.oa?id=169531421003Franco, G. (2017). Modelo de optimización estocástica para explotaciones mineras a cielo abierto. Universidad Nacional de Colombia.Franks, M. D., Boger, D. V., Côte, C. M., & Mulligan, D. . (2011). Sustainable development principles for the disposal of mining and mineral processing wastes. Resources Policy, 36(2), 114–122.Gholamnejad, J. (2008). Determination of the optimum cut-off grade considering environmental cost. Journal of International Environmental Application and Science, 3(3), 186–194.Githiria, J., & Musingwini, C. (2019). A stochastic cut-off grade optimization model to incorporate uncertainty for improved project value. Journal of the Southern African Institute of Mining and Metallurgy, 119(3), 217–228. https://doi.org/10.17159/2411-9717/2019/v119n3a1Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning (1st ed.). Addison-Wesley Longman Publishing Co., Inc.Gómez, E. A., & Díez, J. M. (2015). Evaluación financiera de proyectos (Segunda Ed).Gonzalez, T. (2007). Handbook of Approximation Algorithms and Metaheuristics. Chapman & Hall/CRC.Goodfellow, R. C., & Dimitrakopoulos, R. (2016). Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing, 40, 292–304.Gu, X., Wang, Q., Chu, D., & Zhang, B. (2010). Dynamic optimization of cutoff grade in underground metal mining. 17, 492–497. https://doi.org/10.1007/s11771Gupta, J. N. D., & Sexton, R. S. (1999). Comparing backpropagation with a genetic algorithm for neural network training. Omega, 27(6), 679–684. https://doi.org/10.1016/S0305-0483(99)00027-4Hajkowicz, A. S., Heyenga, S., & Moffat, K. (2011). The relationship between mining and socio-economic well being in Australia’s regions. Resources Policy, 36(1), 30–38.Hall, B. (2014). Cut-off Grades and Optimising the Strategic Mine Plan. The Australasian Institute of Mining and Metallurgy.Hanafi, S., Wang, Y., Glover, F., Yang, W., & Hennig, R. (2023). Tabu search exploiting local optimality in binary optimization. European Journal of Operational Research, 308, 1037–1055. https://doi.org/10.1016/j.ejor.2023.01.001Hassan, R., Cohanim, B., & Weck, O. (2005). A comparison of particle swarm optimization and genetic algorithm. Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, 18–21.He, Y., Xu, S., Zhu, K., Liu, T., & Li, Y. (2008). A Genetic-Neural Method of Optimizing Cut-Off Grade. In Advances in Neural Networks - ISNN 2008 (pp. 588–597). Springer, Berlin, Heidelberg.He, Y., Zhu, K., Gao, S., Liu, T., & Li, Y. (2009). Theory and method of genetic - neural optimization cut-off grade and grade of crude ore. Expert Systems with Applications, 36(4), 7617–7623.Hirai, H., Katamura, K., Mamaclay, F. P., & Fujimura, T. (1987). Development and Mine Operation at Rio Tuba Nickel Mine. International Journal of Mineral Processing, 19, 99–114.Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. The University of Michigan Press/MIT Press.Hosseini-Nasab, E., Khezri, M., Khodamoradi, M. S., & Atashpaz-Gargari, E. (2010). An application of imperialist competitive algorithm to simulation of energy demand based on economic indicators: evidence from Iran. European Journal of Scientific Research, 43(4), 495–506.Izmailov, A. F., & Solodov, M. V. (2013). A globally convergent algorithm for convex programming problems with binary variables. Mathematical Programming, 142(1–2), 233–258.Jöhnk, J., Weißert, M., & Wyrtki, K. (2020). Ready or not, AI comes - an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(5), 20. https://doi.org/https://doi.org/ 10.1007/s12599-020-00676-7Khan, A., & Asad, M. W. A. (2019). A method for optimal cut-off grade policy in open pit mining operations under uncertain supply. Resources Policy, 60, 178–184. https://doi.org/10.1016/j.resourpol.2018.12.003Khan, A., & Asad, M. W. A. (2021). A mixed integer programming based cut-off grade model for open-pit mining of complex poly-metallic resources. Resources Policy, 72, 1–9. https://doi.org/10.1016/j.resourpol.2021.102076Khodayari, A. A., & Jafarnejad, A. (2012). The effect of price changes on optimum cut-off grade of different open-pit mines. Journal of Mining & Environment, 3(1), 61–68.Khodayari, A., & Jafarnejad, A. (2012). Cut-off grade optimization for maximizing the output rate. International Journal of Mining and Geo-Engineering, 46(1), 51–56.King, B. (2001). Optimal Mine Scheduling Policies. London University, UK.King, B. (2011). Optimal mining practice in strategic planning. Journal of Mining Science, 47(2), 247–253.King, B. (2018). Optimal Mining Principles. In R. Dimitrakopoulos (Ed.), Advances in Applied Strategic Mine Planning (1st ed., pp. 19–30).Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.Kumral, M. (2013). Optimizing ore-waste discrimination and block sequencing through simulated annealing. Applied Soft Computing, 13(8), 3737–3744.Lane, K. F. (1964). Choosing the optimum cut-off grade. Colorado School of Mines Quarterly, 59, 811–829.Lane, K. F. (1988). The economic definition of ore: Cut-off grade in theory and practice. Mining Journal Books.Laurence, D. (2011). A Guide to Leading Practice Sustainable Development in Mining. Australian Government Department of Resources, Energy and Tourism.Li, S., & Yang, C. (2012). An optimal algorithm for cut-off grade calculation using multistage stochastic programming. Journal of Theoretical and Applied Information Technology, 45(1), 117–122.Liu, D., Li, G., Hu, N., Xiu, G., & Ma, Z. (2019). Optimization of the cut-off grade for underground polymetallic mines. Gospodarka Surowcami Mineralnymi / Mineral Resources Management, 35(1), 25–42. https://doi.org/10.24425/gsm.2019.128198Mahase, M., Musingwini, C., & Nhleko, A. (2016). A survey of applications of multi-criteria decision analysis methods in mine planning and related case studies. Journal of the Southern African Institute of Mining and Metallurgy, 11, 1051–1056.Maldonado, C. E., & Gómez, N. A. (2011). El mundo de las ciencias de la complejidad. Universidad El Rosario.Malik, H., Iqbal, A., Joshi, P., Agrawal, S., & Bakhsh, F. (2021). Metaheuristic and Evolutionary Computation: Algorithms and Applications (Springer (ed.); 1st ed.).Mansouri, M., Osanloo, M., & Gheisari, N. (2014). Establishing a Sustainable Model to Reduce the Risk of Mine Closure. Mine Planning and Equipment Selection, 1427–1436. https://doi.org/http: //dx.doi.org/10.1007/978-3-319-02678-7_137Marques, D. M., & Costa, J. F. C. L. (2013). An algorithm to simulate ore grade variability in blending and homogenization piles. International Journal of Mineral Processing, 120, 48–55.Martinelli, R., Collard, J., & Gamache, M. (2020). Strategic planning of an underground mine with variable cut-off grades. Optimization and Engineering, 21(3), 803–849. https://doi.org/10.1007/s11081-019-09479-6McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 15–33.McInerney, M., & Dhawan, A. P. (1993). Use of genetic algorithms with backpropagation in training of feedforward neural networks. Proceedings of the IEEE International Conference on Neural Networks, 203–208.Melián, B., Moreno, J. A., & Moreno, J. M. (2003). Metaheurísticas: Una visión global. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 7(19), 7–28.Michalewicz, Z., & Fogel, D. B. (2004). How to Solve It: Modern Heuristics (2nd ed.). Springer Berlin, Heidelberg. https://doi.org/https://doi.org/10.1007/978-3-662-07807-5Minnitt, R. C. A. (2003). Cut-off grade determination for the maximum value of a small Wits-type gold mining operation. Proceedings of the 31st International Symposium on Application of Computers and Operations Research in the Minerals Industries (APCOM).Mishra, B. (2006). Development of a Computer Model for Determination of Cut off Grade for Metalliferous Deposits. Journal of Mines, Metals and Fuels, 54, 147–152.Mohammad, W. A. (1997). Multi-mineral cut-off grade optimization with option to stockpile. Colorado School of Mines.Mohammadi, S., Ataei, M., Kakaie, R., & Pourzamani, E. (2015). Comparison of golden section search method and imperialist competitive algorithm for optimization cut-off grade - case study: Mine No. 1 of Golgohar. Journal of Mining & Environment, 6, 63–71.Mohammadi, S., Kakaie, R., Ataei, M., & Pourzamani, E. (2017). Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm ( ICA ). Resources Policy, 51(July 2016), 39–48. https://doi.org/10.1016/j.resourpol.2016.11.005Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. The MIT Press.Mutti, D., Yakovleva, N., Vazquez-Brust, D., & Di Marco, H. M. (2012). Corporate social responsibility in the mining industry: Perspectives from stakeholder groups in Argentina. Resources Policy, 37(2), 212–222.Myburgh, C. A., Deb, K., & Craig, S. (2014). Applying Modern Heuristics to Maximizing NPV through Cutoff grade Optimization. Orebody Modelling and Strategic Planning Conference, 1–16.Narendran, T. V., & Weinelt, B. (2017). Digital transformation initiative mining and metals industry.Newman, A. M., Rubio, E., Caro, R., Weintraub, A., & Eurek, K. (2010). A review of operations research in mine planning. Interfaces, 40(3), 222–245. https://doi.org/10.1287/inte.1090.0492Niemann-Delius, C., & Sattarvand, J. (2008). Perspective of metaheuristic optimization methods in open pit production planning. Mineral Resources Management = Gospodarka Surowcami Mineralnymi, 24(4,2), 143–155.Noriega, R., & Pourrahimian, Y. (2022). A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning. Resources Policy, 77, 102727. https://doi.org/10.1016/j.resourpol.2022.102727Oliva-Romero, Y., Ochoa-Zezatti, A., Marcela-Herrera, A., & Oliva-Navarro, D. A. (2017). Modelo innovador para un aparador comercial usando un algoritmo competitivo imperialista. Research in Computing Science, 134(1), 35–44. https://doi.org/10.13053/rcs-134-1-3Osanloo, M., Ataei, M. (2003). Using Equivalent Grade Factors to Find the Optimum Cut-off Grades of Multiple Metal Deposits. Minerals Engineering, 16, 771–776.Osanloo, M., Rashidinejad, F., & Rezai, B. (2008). Incorporating environmental issues into optimum cut-off grades modelling at porphyry copper deposits. Resources Policy, 33(4), 222–229.Paithankar, A., Chatterjee, S., & Goodfellow, R. (2021). Open-pit mining complex optimization under uncertainty with integrated cut-off grade based destination policies. Resources Policy, 70, 101875. https://doi.org/10.1016/j.resourpol.2020.101875Prior, T., Giurco, D., Mudd, G., Mason, L., & Behrisch, J. (2012). Resource depletion, peak minerals and the implications for sustainable resource management. Global Environmental Change, 22(3), 577–587. https://doi.org/https://doi.org/10.1016/j.gloenvcha.2011.08.009.Rafiee, R., Ataei, M., & Azarfar, A. (2016). Determination of optimal open-pit mines with the goal of maximizing net present value using colonial competition algorithm. Journal of Analytical and Numerical Methods in Mining Engineering, 11, 89–99.Rahimi, E., & Ghasemzadeh, H. (2015). A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects. Resources Policy, 46(1), 51–63.Rashidinejad, F., Osanloo, M., & Rezai, B. (2008). Cutoff grades optimization with environmental management: a case study: Sungun copper project, IUST. International Journal of Engineering Science, 19, 1–13.Rendu, J.-M. (2014). An introduction to cut-off grade estimation. Society for Mining, Metallurgy, and Exploration (SME).Rule, C. M., Fouchee, R. J., & Swart, W. C. E. (2015). Run of mine ore upgrading–proof of concept plant for XRF ore sorting. Proceedings of the 6th International Conference on Semi-Autogenous and High Pressure Grinding Technology.Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (Pearson (ed.); 4th ed.).Schwartz, E. S. (1977). The valuation of warrants: Implementing a new approach. Journal of Financial Economics, 4(1–2), 79–93.Sexton, R. S., Dorsey, R. E., & Johnson, J. D. (1998). Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decision Support Systems, 22(2), 171–185.Sganzerla, C., Seixas, C., & Conti, A. (2016). Disruptive innovation in digital mining. Procedia Engineering, 138, 64–71.Shinkuma, T., & Nishiyama, T. (2000). The grade selection rule of the metal mines; an empirical study on copper mines. Resources Policy, 26(1), 31–38.Sotoudeh, F., Nehring, M., Kizil, M., Knights, P., & Mousavi, A. (2021). A novel cut-off grade method for increasing the sustainability of underground metalliferous mining operations. Minerals Engineering, 172, 107168. https://doi.org/10.1016/j.mineng.2021.107168Tahmasebi, P., & Hezarkhani, A. (2009). Application of Optimized Neural Network by Genetic Algorithm. In IAMG09. Stanford University.Taylor, H. K. (1972). General background theory of cut-off grades. In Transactions of the Institution of Mining and Metallurgy (pp. A160–A179).Thompson, M., & Barr, D. (2014). Cut-off grade: A real options analysis. Resources Policy, 42, 83–92. https://doi.org/10.1016/j.resourpol.2014.10.003Topp, V., Soames, L., Parham, D., & Bloch, H. (2008). Productivity in the Mining Industry: Measurement and Interpretation.Turner-Saad. (2011). A cut-off of liberated and selected ore minerals optimisation based on the geometallurgy concept. Proceedings of the First AUSIMM International Geometallurgy Conference.Uqaili, A. M., & Harijan, K. (2012). Energy, Environment and Sustainable Development (1st ed.). Springer Vienna. https://doi.org/https://doi.org/10.1007/978-3-7091-0109-4Vallee, M. (2000). Mineral resource + engineering, economic and legal feasibility = ore reserve. CIM Bulletin, 93, 53–61.Wheeler, A. J., & Rodrigues, R. L. (2002). Cutoff-grade analysis at Fazenda Brasileiro: Mine planning for declining gold prices. Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology, 111(1), 35–46.Xie, Y. L. (1998). Optimization of Cut-off Grade in Open-pit Based on Control Theory. Transactions of Nonferrous Metals Society of China, 8, 353–356.Yang, X. S. (2013). Optimization and Metaheuristic Algorithms in Engineering. Metaheuristics in Water, Geotechnical and Transport Engineering, 1–23. https://doi.org/10.1016/B978-0-12-398296-4.00001-5Young, A., & Rogers, P. (2019). A review of digital transformation in mining. Mining, Metallurgy & Exploration, 36, 683–699.EstudiantesInvestigadoresMaestrosORIGINAL1037648048.2023.pdf1037648048.2023.pdfTesis de Maestría en Ingeniería - Recursos Mineralesapplication/pdf1864137https://repositorio.unal.edu.co/bitstream/unal/84945/4/1037648048.2023.pdfda83e70ca8fa8e67a80f472cb7373c97MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84945/5/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD55unal/84945oai:repositorio.unal.edu.co:unal/849452023-11-14 15:01:56.613Repositorio Institucional Universidad Nacional de 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