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Advanced Analytics for Surface Mine Planning

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Advanced Analytics in Mining Engineering

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Abstract

Planning is one of the fundamental tasks for surface mine projects, and it can play a critical role in the success and results obtained after the development and operationalization of the project. This role will be more important when the miners need to go deeper to access the ore, especially when its grade tends to decrease. The quality of the surface mine planning decision depends on the available data, information, and experience of the decision makers, being imperative that robust decision criteria, new technologies and advanced analytics tools are employed in order to make the most of the huge amount of data generated at each instant. This chapter focuses on advanced analytics approaches such as machine learning and artificial intelligence as valuable decision-making tools to improve the surface mine planning process, as well as mathematical optimization techniques already successfully employed in solving ultimate pit and production scheduling problems, using rigorous algorithms, heuristics and metaheuristics. This chapter also covers stochastic mine planning and discusses future mine planning.

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References

  1. Cherchenevski, P.K., J.F.C.L. Costa, and R.H. Rubio. 2019. Grade uncertainty embedded in long term scheduling: Stochastic mine planning. REM International Engineering Journal 72: 275–284. https://doi.org/10.1590/0370-44672018720119.

    Article  Google Scholar 

  2. Abzalov, M. 2016. Conversion resources to reserves. In Applied Mining Geology, ed. M. Abzalov, 365–372. Modern Approaches in Solid Earth Sciences 12. Cham: Springer. https://doi.org/10.1007/978-3-319-39264-6_29.

  3. Monkhouse, P.H.L., and G.A. Yeates. 2018. Beyond naïve optimization. In Advances in Applied Strategic Mine Planning, ed. R. Dimitrakopoulos, 3–18. Cham: Springer. https://doi.org/10.1007/978-3-319-69320-0.

  4. Osanloo, M., J. Gholamnejad, and B. Karimi. 2008. Long-term open pit mine production planning: A review of models and algorithms. International Journal of Mining, Reclamation and Environment 22 (1): 3–35. https://doi.org/10.1080/17480930601118947.

    Article  Google Scholar 

  5. Newman, A.M., E. Rubio, R. Caro, A. Weintraub, and K. Eurek. 2010. A review of operations research in mine planning. Interfaces 40: 222–245. https://doi.org/10.1287/inte.1090.0492.

    Article  Google Scholar 

  6. Whittle, J. 1989. The Facts and Fallacies of Open-Pit Optimization. Balwyn North, Victoria: Whittle Programming Pty Ltd.

    Google Scholar 

  7. Dagdelen, K. 2001. Open pit optimization—Strategies for improving the economics of mining projects through mine planning. In Proceedings of the 17th International Mining Congress and Exhibition of Turkey, IMCET 2001, Ankara.

    Google Scholar 

  8. Axelson, A.H. 1964. A practical approach to computer utilization in mine planning. In Proceedings of the 4th International APCOM Symposium, Golden.

    Google Scholar 

  9. Canessa, G., E. Moreno, and B.K. Pagnoncelli. 2020. The risk-averse ultimate pit problem. Optimization and Engineering. https://doi.org/10.1007/s11081-020-09545-4.

    Article  Google Scholar 

  10. Campos, P.H.A., Arroyo, C.F., and N. Morales. 2018. Application of optimized models through direct block scheduling in traditional mine planning. Journal-South African Institute of Mining and Metallurgy 118 (4): 381–386. https://doi.org/10.17159/2411-9717/2018/v118n4a8.

  11. Erikson, J.D. 1968. Long-range open-pit planning. Mining Engineering 20 (4): 75–78.

    Google Scholar 

  12. Soderberg, A., and D.O. Rausch. 1968. Pit planning and layout. In Surface Mining, ed. E.P. Pfleider, 141–165. New York: SME-AIME.

    Google Scholar 

  13. Koskiniemi, B.C. 1979. Hand methods. In Open-Pit Mine Planning and Design, ed. J.T. Crawford and A. Hustrulid, 187–195. New York: SME/AIME.

    Google Scholar 

  14. Hustrulid, W., M. Kuchta, and R. Martin. 2013. Open Pit Mine Planning and Design, 3rd ed. Leiden: CRC Press.

    Google Scholar 

  15. Luke, K.W. 1972. Functional optimization of open-pit mine design utilizing geologic cross-section data. Transactions of the Metallurgical Society of AIME 252: 125–131.

    Google Scholar 

  16. Pyrcz, M.J., and C.V. Deutsch. 2014. Geostatistical Reservoir Modeling, 2nd ed. New York: Oxford University Press.

    Google Scholar 

  17. Gilani, S.O., and J.A. Sattarvand. 2015. A new heuristic non-linear approach for modeling the variable slope angles in open pit mine planning algorithms. Acta Montanistica Slovaca 20 (4): 251–259.

    Google Scholar 

  18. Kim, Y.C. 1978. Ultimate pit limit design methodologies using computer models, state of the art. Mining Engineering 30 (10): 1454–1459.

    Google Scholar 

  19. Pana, M.T. 1965. The simulation approach to open-pit design. In Proceeding of the 5th International APCOM Symposium, Tucson.

    Google Scholar 

  20. Lemieux, M. 1979. Moving cone optimizing algorithm. In Computer Methods for the 80s in the Mineral Industry, ed. A. Weiss, 329–345. New York: SME/AIME.

    Google Scholar 

  21. Wright, E.A. 1999. Moving cone II—A simple algorithm for optimum pit limits design. In Proceedings of the 28th International APCOM Symposium, Colorado.

    Google Scholar 

  22. Khalokakaie, R. 2007. Optimum open pit design with modified moving cone II methods. Journal of Faculty of Engineering 41 (3): 297–307.

    Google Scholar 

  23. Galić, I., B. Janković, and I. Mrakovčić. 2009. An another way for open-pit mine design optimization: Floating slopes method. Rudarsko-Geolosko-Naftni Zbornik 21: 103–111.

    Google Scholar 

  24. Elahi Zeyni, E., R. Kakaie, and A. Yousefi. 2012. A new algorithm for optimum open pit design: Floating cone method III. Journal of Mining and Environment 2 (2): 118–125.

    Google Scholar 

  25. Korobov, S. 1974. Method for determining optimal open pit limits. Technical Report (EP74-R-4), Ecole Polytechnique de Montreal.

    Google Scholar 

  26. Dowd, P.A., and A.H. Onur. 1992. Optimizing open pit design and sequencing. In Proceeding of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  27. Dowd, P.A., and A.H. Onur. 1993. Open pit optimization—Part 1: Optimal open pit design. Transactions of the Institution of Mining and Metallurgy 102 (A): A95–A104.

    Google Scholar 

  28. Khalokakaie, R. 1999. Computer-aided optimal open pit design with variable slope angles. PhD thesis, University of Leeds.

    Google Scholar 

  29. Shishvan, M.S., and J.A. Sattarvand. 2012. Modeling of accurate variable slope angles in open pit mine design using spline interpolation. Archives of Mining Sciences 57 (4): 921–932. https://doi.org/10.2478/v10267-012-0061-y.

    Article  Google Scholar 

  30. Xu, X.C., X.W. Gu, Q. Wang, J.P. Liu, and J. Wang. 2014. Ultimate pit optimization with the ecological cost for open-pit metal mines. Transactions of the Nonferrous Metals Society of China 24 (5): 1531–1537. https://doi.org/10.1016/S1003-6326(14)63222-2.

    Article  Google Scholar 

  31. Sadeghi, M., H. Dehghani, and B.J. Shokri. 2021. Ultimate pit limit determination using flashlight algorithm. International Journal of Mining and Geo-Engineering 55 (1): 41–46. https://doi.org/10.22059/IJMGE.2020.296120.594840.

    Article  Google Scholar 

  32. Lerchs, H., and I.F. Grossmann. 1965. Optimum design of open pit mines. CIM Bulletin 58: 17–24.

    Google Scholar 

  33. Huttagosol, P. 1988. Modified tree graph algorithm for ultimate pit limit analysis. MSc thesis, Colorado School of Mines.

    Google Scholar 

  34. Johnson, T.B., and R.W. Sharp. 1971. Three-dimensional dynamic programming method for optimal ultimate pit design. USBM Report of Investigation 7553.

    Google Scholar 

  35. Barnes, R.J. 1982. Optimizing the ultimate pit. MSc thesis, Colorado School of Mines.

    Google Scholar 

  36. Koenigsberg, E. 1982. The optimum contours of an open-pit mine: An application of dynamic programming. In Proceedings of the 17th International APCOM Symposium, Golden.

    Google Scholar 

  37. Wilke, F.L., and E.A. Wright. 1984. Determining the ultimate optimal pit for hard rock open pit mines using dynamic programming. Erzmetall 37: 138–144.

    Google Scholar 

  38. Shenggui, Z., and A.M. Starfield. 1985. Dynamic programming with color graphics smoothing for open-pit design on a personal computer. International Journal of Mining Engineering 3: 27–34.

    Article  Google Scholar 

  39. Wright, E.A. 1987. The use of dynamic programming for open pit mine design, some practical implications. Mining Science and Technology 4: 97–104. https://doi.org/10.1016/S0167-9031(87)90214-3.

    Article  Google Scholar 

  40. Yamatomi, J., G. Mogi, A. Akaike, and U. Yamaguchi. 1995. Selective extraction dynamic cone algorithm for three-dimensional open pit designs. In Proceedings of the 25th International APCOM Symposium, Brisbane.

    Google Scholar 

  41. Bond, G.D. 1995. A mathematical analysis of the Lerch and Grossmann algorithm and the nested Lerch and Grossmann algorithm. PhD thesis, Colorado School of Mines.

    Google Scholar 

  42. Vallet, R. 1976. Optimisation mathematique de l’exploitation d’une mine a ciel ouvert ou le problem de l’enveloppe. Annales des Mines de Belgique 113–135.

    Google Scholar 

  43. Hochbaum, D.S., and A. Chen. 2000. Performance analysis and best implementations of old and new algorithms for the open-pit mining problem. Operational Research: An International Journal 48 (6): 894–914. https://doi.org/10.1287/opre.48.6.894.12392.

    Article  Google Scholar 

  44. Zhao, Y. 1992. Algorithms for optimum design and planning of open-pit mines. PhD thesis, University of Arizona.

    Google Scholar 

  45. Zhao, Y., and Y.C. Kim. 1992. A new ultimate pit limit design algorithm. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  46. Johnson, T.B. 1968. Optimum open-pit mine production scheduling. PhD thesis, University of California.

    Google Scholar 

  47. Picard, J.C. 1976. Maximal closure of a graph and applications to combinatorial problems. Management Science 22 (11): 1268–1272. https://doi.org/10.1287/mnsc.22.11.1268.

    Article  MathSciNet  MATH  Google Scholar 

  48. Sahni, J. 1996. Application of push-relabel and heuristics to open-pit mines. MSc thesis, University of British Columbia.

    Google Scholar 

  49. Cai, W. 1989. Application of network flow and zero-one programming to open pit mine design problems. PhD thesis, University of Arizona.

    Google Scholar 

  50. Faaland, B., K. Kim, and T. Schmitt. 1990. A new algorithm for computing the maximal closure. Management Science 36 (3): 315–331. https://doi.org/10.1287/mnsc.36.3.315.

    Article  MathSciNet  MATH  Google Scholar 

  51. Giannini, L.M., L. Caccetta, P. Kelsey, and S. Carras. 1991. PITOPTIM: A new high-speed network flow technique for optimum pit design facilitating rapid sensitivity analysis. AusIMM Proceedings 2: 57–62.

    Google Scholar 

  52. Yegulalp, T.M., and J.A. Arias. 1992. A fast algorithm to solve the ultimate pit limit problem. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  53. Caccetta, L., L.M. Giannini, and P. Kelsey. 1994. On the implementation of exact optimization techniques for open pit design. Asia-Pacific Journal of Operational Research 11: 155–170.

    MATH  Google Scholar 

  54. Caccetta, L. 2007. Application of optimization techniques in open-pit mining. In Handbook of Operations Research in Natural Resources, vol. 99, ed. A. Weintraub et al., 547–559. International Series in Operations Research & Management. Boston: Springer. https://doi.org/10.1007/978-0-387-71815-6_29.

  55. Underwood, R., and B. Tolwinski. 1996. A mathematical programming viewpoint for solving the ultimate pit problem. European Journal of Operational Research 107 (1): 96–107. https://doi.org/10.1016/S0377-2217(97)00141-0.

    Article  MATH  Google Scholar 

  56. Hochbaum, D.S. 2001. A new-old algorithm for minimum-cut and maximum-flow in closure graphs. Networks 37 (4): 171–193. https://doi.org/10.1002/net.1012.

    Article  MathSciNet  MATH  Google Scholar 

  57. Hochbaum, D.S. 2008. The pseudo flow algorithm: A new algorithm for the maximum flow problem. Operations Research 56 (4): 992–1009. https://doi.org/10.1287/opre.1080.0524.

    Article  MathSciNet  MATH  Google Scholar 

  58. Mwangi, A.D., Z. Jianhua, H. Gang, R.M. Kasomo, and M.M. Innocent. 2020. Ultimate pit limit optimization using Boykov-Kolmogorov maximum flow algorithm. Journal of Mining and Environment 12 (1): 1–13. https://doi.org/10.22044/JME.2020.10170.1953.

  59. Meyer, M. 1969. Applying linear programming to the design of ultimate pit limits. Management Science 16 (2): B121–B135. https://doi.org/10.1287/mnsc.16.2.B121.

    Article  Google Scholar 

  60. Gershon, M.E. 1983. Optimal mine production scheduling evaluation of large scale mathematical programming approaches. International Journal of Mining Engineering 1 (4): 315–329.

    Article  Google Scholar 

  61. Huttagosol, P., and A. Cameron. 1992. A computer design of ultimate pit limit by using transportation algorithm. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  62. Underwood, R., and B. Tolwinski. 1998. A mathematical programming viewpoint for solving the ultimate pit problem. European Journal of Operational Research 107 (1): 96–107.

    Article  Google Scholar 

  63. Khodayari, A.A. 2013. A new algorithm for determining ultimate pit limits based on network optimization. International Journal of Mining and Geo-Engineering 47 (2): 129–137. https://doi.org/10.22059/ijmge.2013.51334.

    Article  Google Scholar 

  64. Matheron, G. 1975. Paramétrage des contours optimaux. Centre de Géostatistique et de Morphologie mathématique. Internal Report N-403; Note géostatistique 128, 54 pp, Fontainebleau.

    Google Scholar 

  65. François-Bongarçon, D., and A. Marechal. 1976. A new method for open pit design: Parameterization of the final pit contour. In Proceedings of the 14th International APCOM Symposium, New York.

    Google Scholar 

  66. François-Bongarçon, D., and D. Guibal. 1982. Algorithms for parameterizing reserves under different geometrical constraints. In Proceedings of the 17th International APCOM Symposium, New York.

    Google Scholar 

  67. François-Bongarçon, D. 1994. Myth and reality: A status report on computer open-pit optimization algorithms in the 1990s. In Mining Latin America: Challenges in the Mining Industry, 77–87. London: Chapman & Hall.

    Google Scholar 

  68. Dagdelen, K., and D. François-Bongarçon. 1982. Towards the complete double parameterization of recovered reserves in open pit mining. In Proceedings of the 17th International APCOM Symposium, New York.

    Google Scholar 

  69. Whittle, J. 1988. Beyond optimization in open pit design. Whittle programming. In Proceedings of the 1st Canadian Conference on Computer Applications in the Mineral Industry, 331–337. Rotterdam: Balkema.

    Google Scholar 

  70. Coléou, T. 1989. Technical parameterization of reserves for open pit design and mine planning. In Proceedings of the 21st International APCOM Symposium, Littleton.

    Google Scholar 

  71. Sayadi, A.R., N. Fathianpour, and A.A. Mousavi. 2011. Open pit optimization in 3D using a new artificial neural network. Archives of Mining Sciences 56 (3): 389–403.

    Google Scholar 

  72. Denby, B., D. Schofield, and S. Bradford. 1991. Neural network applications in mining engineering. University of Nottingham Department of Engineering Magazine 43: 13–23.

    Google Scholar 

  73. Achireko, P.K., and S. Frimpong. 1996. Open pit optimization using artificial neural networks on conditionally simulated blocks. In Proceedings of the 26th International APCOM Symposium, State College.

    Google Scholar 

  74. Frimpong, S., and P.K. Achireko. 1997. The MCS/MFNN algorithm for open pit optimization. International Journal of Surface Mining, Reclamation and Environment 11: 45–52. https://doi.org/10.1080/09208119708944055.

    Article  Google Scholar 

  75. Frimpong, S., J.M. Whiting, and J. Szymanski. 1998. Stochastic-optimization annealing of an intelligent, open pit design. Mineral Resources Engineering 7 (1): 15–27.

    Article  Google Scholar 

  76. Achireko, P.K. 1998. Application of modified conditional simulation and artificial neural networks to open-pit optimization. PhD thesis, Dalhousie University Caltech.

    Google Scholar 

  77. Asa, E. 2002. An intelligent 3-D open pit design and optimization using machine learning: Adaptive logic networks and neuro-genetic algorithms. PhD thesis, University of Alberta.

    Google Scholar 

  78. Frimpong, S., J. Szymanski, and A. Narsing. 2002. An intelligent computational algorithm for surface mine layouts optimization. SIMULATION 78 (10): 600–611. https://doi.org/10.1177/0037549702078010002.

    Article  Google Scholar 

  79. Yang, Z.Y., L.S. Lowndes, and B. Denby. 1999. Genetic algorithm optimization of a large U.K. coal mine ventilation network. In Proceedings of the 8th US Mine Ventilation Symposium, Rolla.

    Google Scholar 

  80. Sattarvand, J., S.O. Gilani, M.S. Shishvan, and A. Khan. 2015. Application of the metaheuristic approaches in open pit mine planning. In Proceedings of the 37th International APCOM Symposium, Fairbanks.

    Google Scholar 

  81. Alipour, A., A.A. Khodaiari, A. Jafari, and R. Tavakkoli-Moghaddam. 2020. Production scheduling of open-pit mines using genetic algorithm: A case study. International Journal of Management Science and Engineering Management 15 (3): 176–183. https://doi.org/10.1080/17509653.2019.1683090.

    Article  Google Scholar 

  82. Rosas, J.F.S. 2009. A genetic optimizer for stochastic problems with applications to orebody uncertainty in mine planning. PhD thesis, Laurentian University.

    Google Scholar 

  83. Denby, B., and D. Schofield. 1993. Genetic algorithms: A new approach to pit optimization. In Proceedings of the 24th International APCOM Symposium, Wollongong.

    Google Scholar 

  84. Denby, B., and D. Schofield. 1994. Open pit design and scheduling using genetic algorithms. Transactions of the Institution of Mining and Metallurgy 103 (A): A21–A26.

    Google Scholar 

  85. Denby, B., and D. Schofield. 1995. Inclusion of risk assessment in open pit design and scheduling. Transactions of the Institution of Mining and Metallurgy 104 (A): A67–A71.

    Google Scholar 

  86. Songolo, M. 2010. Pushback design using genetic algorithms. MSc thesis, Curtin University.

    Google Scholar 

  87. Denby, B., D. Schofield, G. Hunter. 1996. Genetic algorithms for open pit scheduling—An extension into 3-dimensions. In Mine Planning and Equipment Selection, ed. W.T. Hennies, L.A. Ayres Da Silva, and A.P. Chaves, 177–185. Rotterdam: A. A. Balkema.

    Google Scholar 

  88. Denby, B., D. Schofield, and T. Surme. 1998. Genetic algorithms for flexible scheduling of open-pit operations. In Proceedings of the 27th International APCOM Symposium, London.

    Google Scholar 

  89. Saleki, M., R. Kakaie, and M. Ataei. 2019. Mathematical relationship between ultimate pit limits generated by discounted and undiscounted block value maximization in open-pit mining. Journal of Sustainable Mining 18: 94–99. https://doi.org/10.1016/j.jsm.2019.03.003.

    Article  Google Scholar 

  90. Caccetta, L., and S. Hill. 2003. An application of branch and cut to open-pit mine scheduling. Journal of Global Optimization 27: 349–365. https://doi.org/10.1023/A:1024835022186.

    Article  MathSciNet  MATH  Google Scholar 

  91. Meagher, C., R. Dimitrakopoulos, and D. Avis. 2014. Optimized open pit mine design, pushbacks, and the gap problem—A review. Journal of Mining Science 50 (3): 508–526. https://doi.org/10.1134/S1062739114030132.

  92. Wang, Q., and H. Sevim. 1995. Alternative to parameterization in finding a series of maximum-metal pits for production planning. Mining Engineering 178–182.

    Google Scholar 

  93. Gershon, M.E. 1987. Heuristic approaches for mine planning and production scheduling. International Journal of Mining and Geo-Engineering 5 (1): 1–13. https://doi.org/10.1007/BF01553529.

    Article  Google Scholar 

  94. Whittle, J. 1998. Beyond optimization in open pit design. In Proceedings of the 1st Canadian Conference on Computer Applications in the Mineral Industry, Quebec.

    Google Scholar 

  95. Ramazan, S., and K. Dagdelen. 1998. A new push-back design algorithm in the open it mining. In Proceedings of the 17th International Symposium on Mine Planning and Equipment Selection, Calgary.

    Google Scholar 

  96. Seymour, F. 1995. Pit limit parametrization from modified 3D Lerchs-Grossmann algorithm. Society of Mining, Metallurgy, and Exploration, Manuscript, 1994.

    Google Scholar 

  97. Roman, R.J. 1971. Mine-mill production scheduling by dynamic programming. The Journal of the Operational Research Society 22 (4): 319–328. https://doi.org/10.1057/jors.1971.76.

    Article  Google Scholar 

  98. Roman, R.J. 1974. The role of the time value of money in determining an open-pit mining sequence and pit limits. In Proceedings of the 12th International APCOM Symposium, Golden.

    Google Scholar 

  99. Wright, E.A. 1989. Dynamic programming in open pit mining sequence planning a case study. In Proceedings of the 21st International APCOM Symposium, New York.

    Google Scholar 

  100. Gershon, M.E., and F.H. Murphy. 1989. Optimizing single hole mine cuts by dynamic programming. European Journal of Operational Research 38 (1): 56–62. https://doi.org/10.1016/0377-2217(89)90468-2.

    Article  Google Scholar 

  101. Onur, A.H., and P.A. Dowd. 1993. Open pit optimization—Part 2: Production scheduling and inclusion of roadways. Transactions of the Institution of Mining and Metallurgy 102 (A): A95–A104.

    Google Scholar 

  102. Tolwinski, B., and R. Underwood. 1992. An algorithm to estimate the optimal evolution of an open-pit mine. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  103. Tan, S., and R. Ramani. 1992. Optimization models for scheduling ore and waste production in open-pit mines. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  104. Elevli, B. 1995. Open pit mine design and extraction sequencing using OR and AI concept. International Journal of Surface Mining, Reclamation and Environment 9 (4): 149–153. https://doi.org/10.1080/09208119508964741.

    Article  Google Scholar 

  105. Tolwinski, B., and T.S. Golosinski. 1995. Long term open pit scheduler. In Proceedings of the International Symposium on Mine Planning and Equipment Selection, Calgary.

    Google Scholar 

  106. Tolwinski, B. 1998. Scheduling production for open-pit mines. In Proceedings of the 27th International APCOM Symposium, London.

    Google Scholar 

  107. Sevin, H., and D.D. Lei. 1998. The problem of production planning in open-pit mines. INFOR 36 (1–2): 1–12. https://doi.org/10.1080/03155986.1998.11732339.

    Article  MATH  Google Scholar 

  108. Erarslan, K., and N. Çelebi. 2001. A simulative model for optimum pit design. CIM Bulletin 94 (1055): 59–68.

    Google Scholar 

  109. Wang, Q., and H. Sun. 2001. A theorem on open-pit planning optimization and its application. In Proceedings of the 29th International APCOM Symposium, Beijing.

    Google Scholar 

  110. Gu, X.W., P.F. Wang, Q. Wang, Y.Y. Zhang, J.P. Liu, and B. Chen. 2011. Simultaneous optimization of final pit and production schedule in open-pit coal mines. Advances in Materials Research 323: 222–228. https://doi.org/10.4028/www.scientific.net/AMR.323.222.

    Article  Google Scholar 

  111. Wang, Q., X.C. Xu, and X.W. Gu. 2013. A dynamic-programming based model for phase-mining optimization in open-pit metal mines. Applied Mechanics and Materials 316–317: 896–901. https://doi.org/10.4028/www.scientific.net/AMM.316-317.896.

    Article  Google Scholar 

  112. Latorre, E., and T.S. Golosinski. 2011. Definition of economic pit limits considering the time value of money. CIM Journal 2 (3): 162–170.

    Google Scholar 

  113. Nanjari, E.L., and T.S. Golosinski. 2013. Optimising open pit mine scheduling considering the time value of money and mining restrictions. International Journal of Mining, Reclamation and Environment 27 (3): 156–165. https://doi.org/10.1080/09658416.2012.655166.

    Article  Google Scholar 

  114. Albach, H. 1967. Long-range planning in open-pit mining. Management Science 13 (10): B549–B568. https://doi.org/10.1287/mnsc.13.10.B549.

    Article  Google Scholar 

  115. Manula, C.B., and H. Gezik. 1968. Application of linear programming in the crushed stone industry. In Proceedings of the Annual Meeting of the American Institute of Mining, Metallurgical, and Petroleum Engineers, New York.

    Google Scholar 

  116. Johnson, T.B. 1969. Optimum production scheduling. In Proceedings of the 8th International APCOM Symposium, Salt Lake City.

    Google Scholar 

  117. Wilke, F., and T. Reimer. 1977. Optimizing the short-term production schedule for an open-pit iron ore mining operation. In Proceedings of the 15th International APCOM Symposium, Brisbane.

    Google Scholar 

  118. Blom, M., A.R. Pearce, and P.J. Stuckey. 2019. Short-term planning for open-pit mines: A review. International Journal of Mining, Reclamation and Environment 33 (5): 318–339. https://doi.org/10.1080/17480930.2018.1448248.

    Article  Google Scholar 

  119. Chanda, E., and F. Wilke. 1992. An EPD model of open-pit short term production scheduling optimization for stratiform orebodies. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  120. Youdi, Z., C. Qingziang, and W. Lixin. 1992. Combined approach for surface mine short term planning optimization. In Proceedings of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  121. Fytas, K., C. Pelley, and P. Calder. 1987. Optimization of open-pit short- and long-range production scheduling. CIM Bulletin 80 (904): 55–61.

    Google Scholar 

  122. Fytas, K., J. Hadjigeorgiou, and J.L. Collins. 1993. Production scheduling optimization in open-pit mines. International Journal of Surface Mining, Reclamation and Environment 7 (1): 1–9. https://doi.org/10.1080/09208119308964677.

    Article  Google Scholar 

  123. Sundar, D.K., and D. Acharya. 1995. Blast schedule planning and shift wise production scheduling of an opencast iron ore mine. Computers and Industrial Engineering 28 (4): 927–935. https://doi.org/10.1016/0360-8352(94)00221-8.

    Article  Google Scholar 

  124. Chanda, E.K.C., and K. Dagdelen. 1995. Optimal blending of mine production using goal programming and interactive graphics systems. International Journal of Surface Mining Reclamation and Environment 9 (4): 203–208. https://doi.org/10.1080/09208119508964748.

    Article  Google Scholar 

  125. Hu, Q., W. Wei, and S. Fang. 1995. Short-term production scheduling for open-pit mines by PERT networks with resource constraints. In Proceedings of the International Symposium on Mine Planning and Equipment Selection, Calgary.

    Google Scholar 

  126. Vujić, S., and G. Ćirović. 1996. Production planning in mines using fuzzy linear programming. Yugoslav Journal of Operations Research 6 (2): 205–215.

    MATH  Google Scholar 

  127. Pendharkar, P.C. 1997. A fuzzy linear programming model for production planning in coal mines. Computers and Operations Research 24 (12): 1141–1149. https://doi.org/10.1016/s0305-0548(97)00024-5.

    Article  MATH  Google Scholar 

  128. Djilani, M.C. 1997. Interactive open-pit design using parameterization techniques. PhD thesis, University of Leeds.

    Google Scholar 

  129. Halatchev, R. 1993. Stage open-cut exploitation of ore deposit. In Proceedings of the 24th International APCOM Symposium, Wollongong.

    Google Scholar 

  130. Halatchev, R. 1996. Factor of compromise in the assessment of open pit long-term production plans. In Proceedings of Surface Mining’96, Johannesburg.

    Google Scholar 

  131. Halatchev, R. 1999. Company strategy—A basis for production scheduling of an open pit complex. In Proceedings of Strategic Mine Planning Conference.

    Google Scholar 

  132. Halatchev, R. 2002. The time aspect of the optimum long-term open-pit production sequencing. In Proceedings of the 30th International APCOM Symposium, Littleton.

    Google Scholar 

  133. Halatchev, R., and E. Kozan. 2003. Coal production scheduling optimization of single seam open-cast mining. In Proceeding of the 5th Large Open Pit Mining Conference, Kalgoorlie.

    Google Scholar 

  134. Kozan, E., and S.Q. Liu. 2011. Operations research for mining: A classification and literature review. ASOR Bulletin 30 (1): 2–23.

    Google Scholar 

  135. Halatchev, R., and P. Lever. 2004. The optimum production equipment scale for open pit coal mining. In Proceedings of the SME Annual Meeting, Denver.

    Google Scholar 

  136. Halatchev, R. 2005. A model of discounted profit variation of open-pit production sequencing optimization. In Application of Computers and Operations Research in the Mineral Industry, ed. S.D. Dessureault, R. Ganguli, V. Kecojevic, and J. Girard-Dwyer, 305–323. London: Taylor & Francis Group.

    Google Scholar 

  137. Halatchev, R. 2015. The spatial aspect of the long-term open pit mine sequence optimization. In Proceedings of the SME Annual Meeting, Denver.

    Google Scholar 

  138. Souza, F.R., L. Soares, H. Burgarelli, A. Nader, C. Arroyo, and L. Alberto. 2018. Direct stockpile scheduling mathematical formulation. DYNA 85 (204): 296–301. https://doi.org/10.15446/dyna.v85n204.62642.

  139. Picard, J.C., and B.T. Smith. 2004. Parametric maximum flows and the calculation of optimal intermediate contours in open pit mine design. INFOR 42 (2): 143–153. https://doi.org/10.1080/03155986.2004.11732697.

    Article  MathSciNet  Google Scholar 

  140. Topal, E., and S. Ramazan. 2012. Strategic mine planning model using network flow model and real case application. International Journal of Mining, Reclamation and Environment 26 (1): 29–37. https://doi.org/10.1080/17480930.2011.600827.

    Article  Google Scholar 

  141. Amankwah, H., T. Larsson, and B. Textorius. 2014. A maximum flow formulation of a multi-period open-pit mining problem. Operational Research: An International Journal 14: 1–10. https://doi.org/10.1007/s12351-013-0140-7.

    Article  MATH  Google Scholar 

  142. Liu, S.Q., and E. Kozan. 2016. New graph-based algorithms efficiently solve large scale open pit mining optimization problems. Expert Systems with Applications 43: 59–65. https://doi.org/10.1016/j.eswa.2015.08.044.

    Article  Google Scholar 

  143. Espinoza, D., M. Goycoolea, E. Moreno, and A. Newman. 2013. MineLib: A library of open pit mining problems. Annals of Operations Research 206: 93–114. https://doi.org/10.1007/s10479-012-1258-3.

    Article  MathSciNet  MATH  Google Scholar 

  144. Smith, M.L., and T.W. You. 1995. Mine production scheduling for optimization of plant recovery in surface phosphate operations. International Journal of Surface Mining Reclamation and Environment 9 (2): 41–46. https://doi.org/10.1080/09208119508964716.

    Article  Google Scholar 

  145. Smith, M.L. 1998. Optimizing short-term production schedules in surface mining: Integrating mine modeling software with ANIPL/CPLEX. International Journal of Surface Mining Reclamation and Environment 12 (4): 149–155. https://doi.org/10.1080/09208118908944038.

    Article  Google Scholar 

  146. Smith, M.L. 1999. Optimizing inventory stockpiles and mine production: An application of separable and goal programming to phosphate mining using AMPL/CPLEX. CIM Bulletin 92 (1030): 61–64.

    Google Scholar 

  147. Rehman, S.U., and M.W.A. Asad. 2010. A mixed-integer linear programming (MILP) model for short-range production scheduling of cement quarry operations. Asia-Pacific Journal of Operational Research 27 (3): 315–333. https://doi.org/10.1142/S0217595910002727.

    Article  MATH  Google Scholar 

  148. Askari-Nasab, H., H. Eivazy, M. Tabesh, and M.M. Badiozamani. 2011. A mathematical programming model for open pit short-term production scheduling. In Proceedings of the SME Annual Meeting, Denver.

    Google Scholar 

  149. Ben-Awuah, E., and H. Askari-Nasab. 2011. Oil sands mine planning and waste management using mixed integer goal programming. International Journal of Mining, Reclamation and Environment 25 (3): 226–247. https://doi.org/10.1080/17480930.2010.549656.

    Article  Google Scholar 

  150. Eivazy, H., and H. Askari-Nasab. 2012. A mixed-integer linear programming model for short-term open pit mine production scheduling. Mining Technology 121 (2): 97–108. https://doi.org/10.1179/1743286312Y.0000000006.

    Article  Google Scholar 

  151. L’Heureux, G., M. Gamache, and F. Soumis. 2013. Mixed integer programming model for short term planning in open-pit mines. Mining Technology 122 (2): 101–109. https://doi.org/10.1179/1743286313Y.0000000037.

    Article  Google Scholar 

  152. Yavarzadeh, S., J. Abodallheisharif, and A. Neishabouri. 2014. Modeling of short term production scheduling with the objective grade control. In Mine Planning and Equipment Selection: Proceedings of the 22nd MPES Conference, Dresden, Germany, ed. C. Drebenstedt, and R. Singhal, 379–387, October 14th–19th, 2013. Cham: Springer. https://doi.org/10.1007/978-3-319-02678-7_37.

  153. Mousavi, A.A., E. Kozan, and S.Q. Liu. 2015. Integrated approach to optimize open-pit mine block sequencing. In Industrial Engineering Non-traditional Applications in International Settings, ed. B.Y. Kara, B. Bidanda, and I. Sabuncuoglu, 83–98. Boca Raton: CRC Press.

    Google Scholar 

  154. Kozan, E., and S.Q. Liu. 2018. An open-pit multi-stage mine production scheduling model for drilling, blasting, and excavating operations. In Advances in Applied Strategic Mine Planning, ed. R. Dimitrakopoulos, 655–668. Cham: Springer. https://doi.org/10.1007/978-3-319-69320-0_38.

  155. Flores-Fonseca, C., R. Linfati, and J.W. Escobar. 2021. Exact algorithms for production planning in mining considering the use of stockpiles and sequencing of power shovels in open-pit mines. Operational Research: An International Journal. https://doi.org/10.1007/s12351-020-00618-x.

    Article  Google Scholar 

  156. Gershon, M.E. 1985. Developments in computerized mine production scheduling. In Proceedings of SME Fall Meeting, Albuquerque.

    Google Scholar 

  157. Gershon, M.E. 1986. A blending-based approach to mine planning and production scheduling. In Proceedings of the 19th International APCOM Symposium, State College.

    Google Scholar 

  158. Wang, Q., and H. Sevim. 1992. Enhanced production planning in open pit mining through intelligent dynamic search. In Proceeding of the 23rd International APCOM Symposium, Tucson.

    Google Scholar 

  159. Wang, Q., and H. Sevim. 1993. Alternative to parameterization in finding a series of maximum-metal pits for production planning. In Proceeding of the 24th International APCOM Symposium, Wollongong.

    Google Scholar 

  160. Sattarvand, J. 2009. Long-term open-pit planning by ant colony optimization. PhD thesis, Rheinisch-Westfälische Technische Hochschule Aachen University.

    Google Scholar 

  161. Sevim, H., and D.D. Lei. 1996. Production planning with working-slope maximum-metal pit sequences. Transactions of the Institution of Mining and Metallurgy 105 (A): A93–A98.

    Google Scholar 

  162. Kumral, M., and P.A. Dowd. 2002. Short-term mine production scheduling for industrial minerals using multi-objective simulated annealing. In Proceedings of the 30th International APCOM Symposium, Littleton.

    Google Scholar 

  163. Kumral, M., and P.A. Dowd. 2005. A simulated annealing approach to mine production scheduling. The Journal of the Operational Research Society 56: 922–930. https://doi.org/10.1057/palgrave.jobs.2601902.

    Article  MATH  Google Scholar 

  164. Samanta, B., A. Bhattacherjee, and R. Ganguli. 2005. A genetic algorithms approach for grade control planning in a bauxite deposit. In Application of Computers and Operations Research in the Mineral Industry, ed. S.D. Dessureault, R. Ganguli, V. Kecojevic, and J. Girard-Dwyer, 337–342. London: Taylor & Francis Group. https://doi.org/10.1201/9781439833407.ch44.

  165. Zhang, M. 2006. Combining genetic algorithms and topological sort to optimize open-pit mine plans. In Mine Planning and Equipment Selection: Proceedings of the 15th MPES Conference, Fiordo, Torino, Italy, ed. M. Cardu, R. Cicco, E. Lovera, and E. Michelotti, 1234–1239.

    Google Scholar 

  166. Ferland, J.A., J. Amaya, and S. Djuimo. 2007. Application of a particle swarm algorithm to the capacitated open-pit mining problem. In Autonomous Robots and Agents—Studies in Computational Intelligence, vol. 76, ed. S.C. Mukhopadhyay, and G.S. Gupta, 127–133. Cham: Springer. https://doi.org/10.1007/978-3-540-73424-6_15.

  167. Shishvan, M.S., and J. Sattarvand. 2015. Long term production planning of open-pit mines by ant colony optimization. European Journal of Operational Research 240 (3): 825–836. https://doi.org/10.1016/j.ejor.2014.07.040.

    Article  MATH  Google Scholar 

  168. Myburgh, C., and K. Deb. 2010. Evolutionary algorithms in large-scale open-pit mine scheduling. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation—GECCO’10, Portland. https://doi.org/10.1145/1830483.1830696.

  169. Khan, A., and C. Niemann-Delius. 2014. Production scheduling of open-pit mines using particle swarm optimization algorithm. Advances in Operations Research 2014: 1–9. https://doi.org/10.1155/2014/208502.

    Article  MathSciNet  Google Scholar 

  170. Khan, A. 2015. Development of new metaheuristic tools for long-term production scheduling of open-pit mines. PhD thesis, Rheinisch-Westfälische Technische Hochschule Aachen University.

    Google Scholar 

  171. Asl, M.M., and J. Sattarvand. 2016. An imperialist competitive algorithm for solving the production scheduling problem in open pit mine. International Journal of Mining and Geo-Engineering 50 (1): 131–143. https://doi.org/10.22059/IJMGE.2016.57862.

  172. Alipour, A., A.A. Khodaiari, A. Jafari, and R. Tavakkoli-Moghaddam. 2017. A genetic algorithm approach for open-pit mine production scheduling. International Journal of Mining and Geo-Engineering 51 (1): 47–52. https://doi.org/10.22059/ijmge.2017.62152.

  173. Ramezanalizadeh, T., M. Monjezi, A.R. Sayadi, and A. Mousavi. 2020. Development of a MIP model to maximize NPV and minimize adverse environmental impact—A heuristic approach. Environmental Monitoring and Assessment 192 (605): 1–15. https://doi.org/10.1007/s10661-020-08550-5.

    Article  Google Scholar 

  174. Pendharkar, P.C., and J.A. Rodger. 2000. Nonlinear programming and genetic search application for production scheduling in coal mines. Annals of Operations Research 95: 251–267. https://doi.org/10.1023/A:1018958209290.

    Article  MATH  Google Scholar 

  175. Souza, M.J.F., I.N. Coelho, S. Ribas, H.G. Santos, and L.H.C. Merschmann. 2010. A hybrid heuristic algorithm for the open-pit-mining operational planning problem. European Journal of Operational Research 207 (2): 1041–1051. https://doi.org/10.1016/j.ejor.2010.05.031.

    Article  MATH  Google Scholar 

  176. Tabesh, M., and H. Askari-Nasab. 2011. Two-stage clustering algorithm for block aggregation in open-pit mines. Mining Technology 120 (3): 158–169. https://doi.org/10.1179/1743286311y.0000000009.

    Article  Google Scholar 

  177. Tabesh, M., and H. Askari-Nasab. 2013. Automatic creation of mining polygons using hierarchical clustering techniques. Journal of Mining Science 49 (3): 426–440. https://doi.org/10.1134/S1062739149030106.

    Article  Google Scholar 

  178. Tabesh, M., C. Mieth, and H. Askari-Nasab. 2013. Open pit production planning using controlled pushbacks and aggregates. In Proceedings of the 23rd World Mining Congress, Montréal.

    Google Scholar 

  179. Tabesh, M., C. Mieth, and H. Askari-Nasab. 2014. A multi-step approach to long-term open-pit production planning. International Journal of Mining and Mineral Engineering 5 (4): 273–298. https://doi.org/10.1504/IJMME.2014.066577.

    Article  Google Scholar 

  180. Tabesh, M., H. Askari-Nasab, and R. Peroni. 2015. A comprehensive approach to strategic open pit mine planning with stockpile consideration. In Proceedings of the 37th International APCOM Symposium, Alaska.

    Google Scholar 

  181. Blom, M.L., C.N. Burt, A.R. Pearce, and P.J. Stuckey. 2014. A decomposition-based heuristic for collaborative scheduling in a network of open-pit mines. INFOR 26 (4): 658–676. https://doi.org/10.1287/ijoc.2013.0590.

    Article  MathSciNet  MATH  Google Scholar 

  182. Blom, M.L., A.R. Pearce, and P.J. Stuckey. 2016. A decomposition-based algorithm for scheduling open-pit networks over multiple periods. Management Science 62 (10): 3059–3084. https://doi.org/10.1287/mnsc.2015.2284.

    Article  Google Scholar 

  183. Mousavi, A., E. Kozan, and S.Q. Liu. 2016. Open-pit block sequencing optimization: A mathematical model and solution technique. Engineering Optimization 48 (11): 1932–1950. https://doi.org/10.1080/0305215X.2016.1142080.

    Article  MathSciNet  Google Scholar 

  184. Mousavi, A., E. Kozan, and S.Q. Liu. 2016. Comparative analysis of three metaheuristics for short-term open pit block sequencing. Journal of Heuristics 22 (3): 301–329. https://doi.org/10.1007/s10732-016-9311-z.

    Article  MATH  Google Scholar 

  185. Maiti, N., P. Pathak, and B. Samanta. 2021. An efficient algorithm for the precedence constraint knapsack problem concerning large-scale open-pit mining pushback design. Mining Technology 1–14. https://doi.org/10.1080/25726668.2020.1866369.

  186. Byun, J., and R. Dimitrakopoulos. 2013. An efficient algorithm for the LP relaxation of the maximal closure problem with a capacity constraint. Les Cahiers du GERAD, G-2013-60

    Google Scholar 

  187. Chicoisne, R., D. Espinoza, M. Goycoolea, E. Moreno, and E. Rubio. 2012. A new algorithm for the open-pit mine production scheduling problem. Operations Research 60 (3): 517–528. https://doi.org/10.1287/opre.1120.1050.

    Article  MathSciNet  MATH  Google Scholar 

  188. Bienstock, D., and M. Zuckerberg. 2010. Solving LP relaxations of large-scale precedence constrained problems. In Integer Programming and Combinatorial Optimization. IPCO 2010, vol. 6080, ed. F. Eisenbrand, and F.B. Shepherd, 1–14. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-13036-6_1.

  189. Moreno, E., D. Espinoza, and M. Goycoolea. 2010. Large-scale multi-period precedence constrained knapsack problem: A mining application. Electronic Notes in Discrete Mathematics 36: 407–414. https://doi.org/10.1016/j.endm.2010.05.052.

    Article  MATH  Google Scholar 

  190. Frimpong, S., E. Asa, and J. Szymanski. 1998. MULSOPS: Multivariate optimized pit shells simulator for tactical mine planning. International Journal of Surface Mining, Reclamation and Environment 12 (4): 163–171. https://doi.org/10.1080/09208118908944040.

    Article  Google Scholar 

  191. Frimpong, S., E. Asa, and R.S. Suglo. 2001. Numerical simulation of surface mine production system using pit shell simulator. Mineral Resources Engineering 10 (2): 185–203. https://doi.org/10.1142/S0950609801000609.

    Article  Google Scholar 

  192. Askari-Nasab, H., K. Awuah-Offei, and S. Frimpong. 2004. Stochastic simulation of open-pit pushbacks with a production simulator. In Proceedings of the CIM Mining Industry Conference and Exhibition, Edmonton.

    Google Scholar 

  193. Askari-Nasab, H., and J. Szymanski. 2005. Modelling open pit dynamics using Monte Carlo simulation. In Proceedings of the 5th Computer Applications in the Minerals Industry, Banff.

    Google Scholar 

  194. Askari-Nasab, H., and J. Szymanski. 2007. Continuous modeling of open-pit dynamics. In Proceedings of the 16th MPES Conference, Bangkok.

    Google Scholar 

  195. Askari-Nasab, H., S. Frimpong, and J. Szymanski. 2008. Investigating the continuous-time open-pit dynamics. Journal-South African Institute of Mining and Metallurgy 108 (2): 61–71.

    Google Scholar 

  196. Askari-Nasab, H., and J. Szymanski. 2007. Open pit production scheduling using reinforcement learning. In Proceedings of the 33rd International APCOM Symposium, Santiago.

    Google Scholar 

  197. Askari-Nasab, H., and K. Awuah-Offei. 2009. Open pit optimization using discounted economic block values. Mining Technology 118 (1): 1–12. https://doi.org/10.1179/037178409X12450752943243.

  198. Aras, C., K. Dagdelen, and T.B. Johnson. 2019. Generating pushbacks using direct block mine production scheduling algorithm. In Mining Goes Digital, ed. C. Mueller et al. London: CRC Press.

    Google Scholar 

  199. Morales, N., E. Jélvez, P. Nancel-Penard, A. Marinho, O. Guimarães. 2015. A comparison of conventional and direct block scheduling methods for open-pit mine production scheduling. In Proceedings of the 37th International APCOM Symposium, Alaska.

    Google Scholar 

  200. Smith, C.E. 1978. The use of mixed-integer programming in planning an alluvial diamond deposit depletion. In Proceedings of the 10th Annual Conference of the Operations Research Society of South Africa, Durban.

    Google Scholar 

  201. Gershon, M.E. 1982. A linear programming approach to mine scheduling optimization. In Proceedings of the 17th International APCOM Symposium, New York.

    Google Scholar 

  202. Gershon, M.E. 1983. Mine scheduling optimization with mixed-integer programming. Mining Engineering 35: 351–354.

    Google Scholar 

  203. Brickey, A.J. 2015. Underground production scheduling optimization with ventilation constraints. PhD thesis, Colorado School of Mines.

    Google Scholar 

  204. Barbaro, R.W., R.V. Ramani, and P.T. Luckie. 1982. Optimal location of and production scheduling for a centralized coal preparation plant. Special Research Report Number SR-116, Coal Research Section, The Pennsylvania State University.

    Google Scholar 

  205. Barbaro, R.W., and R.V. Ramani. 1986. Generalized multiperiod MIP model for production scheduling and processing facilities selection and location. Mining Engineering 38 (2): 107–114.

    Google Scholar 

  206. Dagdelen, K., and T.B. Johnson. 1986. Optimum open pit mine production scheduling by Lagrangian parameterization. In Proceedings of the 19th International APCOM Symposium, State College.

    Google Scholar 

  207. Klingman, D., and N. Phillips. 1988. Integer programming for optimal phosphate-mining strategies. The Journal of the Operational Research Society 39 (9): 805–810. https://doi.org/10.1057/jors.1988.140.

    Article  Google Scholar 

  208. Caccetta, L., P. Kelsey, and L.M. Giannini. 1998. Open-pit mine production scheduling. In Proceedings of the 3rd Regional APCOM Symposium.

    Google Scholar 

  209. Akaike, A., and K. Dagdelen. 1999. A strategic production scheduling method for an open-pit mine. In Proceedings of the 28th International APCOM Symposium, Littleton.

    Google Scholar 

  210. Urbaez, E., and K. Dagdelen. 1999. Implementation of the linear programming model for optimum open pit production scheduling problem. In Proceedings of the SME Annual Meeting, Denver.

    Google Scholar 

  211. Hoerger, S., L. Hoffman, and F. Seymour. 1999. Mine planning at Newmont’s Nevada operations. Mining Engineering 51 (10): 3–7.

    Google Scholar 

  212. Mogi, G., T. Adachi, A. Akaike, and J. Yamatomi. 2001. Optimum production scale and scheduling of open-pit mines using revised 4D network relaxation method. In Proceedings of the 17th International Symposium on Mine Planning and Equipment Selection.

    Google Scholar 

  213. Kawahata, K., P. Schumacher, and K. Criss. 2016. Large-scale mine production scheduling optimization with mill blending constraints at Newmont’s Twin Creeks Operation. Mining Technology 125 (4): 1–5. https://doi.org/10.1080/14749009.2016.1212510.

    Article  Google Scholar 

  214. Ramazan, S. 2001. Open pit mine scheduling based on fundamental tree algorithm. PhD thesis, Colorado School of Mines.

    Google Scholar 

  215. Ramazan, S., and R. Dimitrakopoulos. 2003. Production scheduling optimization in a nickel laterite deposit: MIP and LP applications and infeasibility in the presence of orebody variability. In Proceedings of the 12th International Symposium on Mine Planning and Equipment Selection, Kalgoorlie.

    Google Scholar 

  216. Ramazan, S., and R. Dimitrakopoulos. 2004. Recent operations research applications and efficient MIP formulations in open-pit mining. Transactions of the Society for Mining, Metallurgy and Exploration Inc. 316: 73–78.

    Google Scholar 

  217. Ramazan, S., and R. Dimitrakopoulos. 2004. Traditional and new MIP models for production scheduling with in-situ grade variability. International Journal of Surface Mining and Reclamation and Environment 18 (2): 85–98. https://doi.org/10.1080/13895260412331295367.

    Article  Google Scholar 

  218. Stone, P., G. Froyland, M. Menabde, B. Law, R. Pasyar, and P. Monkhouse. 2004. Blaser-blended iron-ore mine planning optimization at Yandi. In Proceedings of the Orebody Modelling and Strategic Mine Planning International Symposium, Melbourne.

    Google Scholar 

  219. Fricle, C. 2006. Applications of integer programming in open-pit mining. PhD thesis, The University of Melbourne.

    Google Scholar 

  220. Boland, N., C. Fricke, and G. Froyland. 2007. A strengthened formulation for the open-pit mine production scheduling problem. In Optimization Online. Available via DIALOG. http://www.optimization-online.org/DB_FILE/2007/03/1624.pdf. Accessed March 01, 2021.

  221. Boland, N., I. Dumitrescu, G. Froyland, and A.M. Gleixner. 2009. LP-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity. Computers and Operations Research 36 (4): 1064–1089. https://doi.org/10.1016/j.cor.2007.12.006.

    Article  MATH  Google Scholar 

  222. Amaya, J., D. Espinoza, M. Goycoolea, E. Moreno, T. Prevost, and E. Rubio. 2009. A scalable approach to optimal block scheduling. In Proceedings of the 34th International APCOM Symposium, Vancouver.

    Google Scholar 

  223. Bley, A., N. Boland, C. Fricke, and G. Froyland. 2010. A strengthened formulation and cutting planes for the open-pit mine production scheduling problem. Computers and Operations Research 37 (9): 1641–1647. https://doi.org/10.1016/j.cor.2009.12.008.

    Article  MATH  Google Scholar 

  224. Askari-Nasab, H., Y. Pourrahimian, E. Ben-Awuah, and S. Kalantari. 2011. Mixed integer linear programming formulations for open-pit production scheduling. Journal of Mining Science 47 (3): 338–359. https://doi.org/10.1134/S1062739147030117.

    Article  Google Scholar 

  225. Cullenbine, C., R.K. Wood, and A.A. Newman. 2011. A sliding time window heuristic for open pit mine block sequencing. Optimization Letters 5: 365–377. https://doi.org/10.1007/s11590-011-0306-2.

    Article  MathSciNet  MATH  Google Scholar 

  226. Smith, M.L., and S.J. Wicks. 2014. Medium-term production scheduling of the Lumwana mining complex. Interfaces 44 (2): 176–194. https://doi.org/10.1287/inte.2014.0737.

    Article  Google Scholar 

  227. Lambert, W.B., and A.M. Newman. 2014. Tailored Lagrangian relaxation for the open pit block sequencing problem. Annals of Operations Research 222: 419–438. https://doi.org/10.1007/s10479-012-1287-y.

    Article  MathSciNet  MATH  Google Scholar 

  228. Lamghari, A., R. Dimitrakopoulos, and J.A. Ferland. 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. https://doi.org/10.1007/s10898-10014-10185-z.

    Article  MathSciNet  MATH  Google Scholar 

  229. Vossen, T.W.M., R.K. Wood, and A.M. Newman. 2016. Hierarchical benders decomposition for open-pit mine block sequencing. Operations Research 64 (4): 771–793. https://doi.org/10.1287/opre.2016.1516.

    Article  MathSciNet  MATH  Google Scholar 

  230. Aras, C. 2018. A new integer solution algorithm to solve open-pit mine production scheduling problems. PhD thesis, Colorado School of Mines.

    Google Scholar 

  231. Mai, N.L., E. Topal, and O. Erten. 2018. A new open-pit mine planning optimization method using block aggregation and integer programming. Journal-South African Institute of Mining and Metallurgy 118: 705–714. https://doi.org/10.17159/2411-9717/2018/v118n7a4.

  232. Souza, F.R., H.R. Burgarelli, A.S. Nader, C.E.A. Ortiz, L.S. Chaves, L.A. Carvalho, V.F.N. Torres, T.R. Câmara, and R. Galery. 2018. Direct block scheduling technology: Analysis of avidity. REM: International Engineering Journal 71 (1): 97–104. https://doi.org/10.1590/0370-44672017710129.

    Article  Google Scholar 

  233. Samavati, M., D. Essam, M. Nehring, and R. Sarker. 2018. A new methodology for the open-pit mine production scheduling problem. Omega 81: 169–182. https://doi.org/10.1016/j.omega.2017.10.008.

    Article  MATH  Google Scholar 

  234. Jélvez, E., N. Morales, and P. Nancel-Penard. 2019. Open-pit mine production scheduling: improvements to Minelib library problems. In Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection, MPES 2018, ed. E. Widzyk-Capehart, A. Hekmat, and R. Singhal, 223–232. Cham: Springer. https://doi.org/10.1007/978-3-319-99220-4_18.

  235. Jélvez, E., N. Morales, P. Nancel-Penard, and F. Cornillier. 2020. A new hybrid algorithm for the precedence constrained production scheduling problem: A mining application. Omega 94 (11). https://doi.org/10.1016/j.omega.2019.03.004.

  236. Letelier, O.R., D. Espinoza, M. Goycoolea, E. Moreno, and G. Muñoz. 2020. Production scheduling for strategic open pit mine planning: A mixed-integer programming approach. Operations Research 68 (5): 1425–1444. https://doi.org/10.1287/opre.2019.1965.

    Article  MathSciNet  MATH  Google Scholar 

  237. Gholamnejad, J., A. Azimi, R. Lotfian, S. Kasmaeeyazdi, and F. Tinti. 2020. The application of a stockpile stochastic model into long-term open pit mine production scheduling to improve the feed grade for the processing plant. Rudarsko-Geolosko-Naftni Zbornik 35 (4): 115–129. https://doi.org/10.17794/rgn.2020.4.10.

    Article  Google Scholar 

  238. Dimitrakopoulos, R. 1998. Conditional simulation algorithms orebody uncertainty in open pit optimization. International Journal of Mining, Reclamation and Environment 12 (4): 173–179. https://doi.org/10.1080/09208118908944041.

    Article  Google Scholar 

  239. Ramazan, S., and R. Dimitrakopoulos. 2018. Stochastic optimization of long-term production scheduling for open pit mines with a new integer programming formulation. In Advances in Applied Strategic Mine Planning, ed. R. Dimitrakopoulos, 139–153. Cham: Springer. https://doi.org/10.1007/978-3-319-69320-0_11.

  240. Ravenscroft, P.J. 1992. Risk analysis for mine scheduling by conditional simulation. Transactions of the Institution of Mining and Metallurgy 101 (A): A104–A108.

    Google Scholar 

  241. Dowd, P. 1997. Risk in minerals projects: analysis, perception, and management. Transactions of the Institution of Mining and Metallurgy 106 (A): A9–A18.

    Google Scholar 

  242. Smith, M., and R. Dimitrakopoulos. 1999. The influence of deposit uncertainty on mine production scheduling. International Journal of Mining, Reclamation and Environment 13 (4): 173–178. https://doi.org/10.1080/09208119908944244.

    Article  Google Scholar 

  243. Dimitrakopoulos, R., C.T. Farrelly, and M. Godoy. 2002. Moving forward from traditional optimization: Grade uncertainty and risk effects in open-pit design. Transactions of the Institution of Mining and Metallurgy 111 (A): A82–A88.

    Google Scholar 

  244. Ramazan, S., and R. Dimitrakopoulos. 2003. Production scheduling optimization in nickel laterite deposit: MIP and LP application and in the presence of orebody variability mine. In Proceedings of the 12th MPES Conference, Kalgoorlie.

    Google Scholar 

  245. Dimitrakopoulos, R., and S. Ramazan. 2004. Uncertainty-based production scheduling in open pit mining. Transactions of the Society for Mining, Metallurgy and Exploration Inc. 316: 106–112.

    Google Scholar 

  246. Menabde, M., G. Froyland, P. Stone, and G. Yeates. 2004. Mining schedule optimization for conditionally simulated orebodies. In Proceedings of the International Symposium on Orebody Modelling and Strategic Mine Planning: Uncertainty and Risk Management, Perth.

    Google Scholar 

  247. Godoy, M., and R. Dimitrakopoulos. 2004. Managing risk and waste mining in long-term production scheduling of open-pit mines. Transactions of the Society for Mining, Metallurgy and Exploration Inc. 316: 43–50.

    Google Scholar 

  248. Ramazan, S., and R. Dimitrakopoulos. 2004. Stochastic optimization of long-term production scheduling for open pit mines with a new integer programming formulation. In Proceedings of the International Symposium on Orebody Modelling and Strategic Mine Planning: Uncertainty and Risk Management, Perth.

    Google Scholar 

  249. Ramazan, S., and R. Dimitrakopoulos. 2003. Stochastic integer programming based modeling for long-term production scheduling of open-pit mines. ARC-Linkage Report N-6002-1. University of Queensland, Brisbane.

    Google Scholar 

  250. Ramazan, S., R. Dimitrakopoulos, J. Benndorf, and L. Archambeault. 2004. Extension of SIP modeling for long-term production scheduling with stochastically designed stockpiles and multiple ore processors. ARC-Linkage Report N-6003-1, W H Bryan Mining Geology Research Centre, University of Queensland, Brisbane.

    Google Scholar 

  251. Dimitrakopoulos, R., and S. Ramazan. 2008. Stochastic integer programming for optimizing long term production schedules of open-pit mines: Methods, application, and value of stochastic solutions. Mining Technology 117 (4): 155–160. https://doi.org/10.1179/174328609X417279.

    Article  Google Scholar 

  252. Ramazan, S., and R. Dimitrakopoulos. 2013. Production scheduling with uncertain supply: A new solution to the open-pit mining problem. Optimization and Engineering 14 (2): 361–380. https://doi.org/10.1007/s11081-012-9186-2.

    Article  MATH  Google Scholar 

  253. Jalali, S.E., M. Ataee-pour, and K. Shahria. 2006. Pit limit optimization using stochastic process. CIM Bulletin 99 (1096): 1–11.

    Google Scholar 

  254. Golamnejad, J., M. Osanloo, and B. Karimi. 2006. A chance-constrained programming approach for open pit long-term production scheduling in stochastic environments. Journal of the South African Institute of Mining and Metallurgy 106: 105–114.

    Google Scholar 

  255. Leite, A., and R. Dimitrakopoulos. 2007. A stochastic optimization model for open pit mine planning: Application and risk analysis at a copper deposit. Mining Technology 116 (3): 109–118. https://doi.org/10.1179/174328607X228848.

    Article  Google Scholar 

  256. Kent, M., R. Peattie, and V. Chamberlain. 2007. Incorporating grade uncertainty in the decision to expand the main pit at the Navachab gold mine, Namibia, through stochastic simulation. AusIMM Spectrum Series 14: 207–218.

    Google Scholar 

  257. Dimitrakopoulos, R., L. Martinez, and S. Ramazan. 2007. A maximum upside/minimum downside approach to the traditional optimization of open-pit mine design. Journal of Mining Science 43: 73–82. https://doi.org/10.1007/s10913-007-0009-3.

    Article  Google Scholar 

  258. Dimitrakopoulos, R., L. Martinez, and S. Ramazan. 2007. Optimising open pit design with simulated orebodies and Whittle Four-X—A maximum upside/minimum downside approach. In Australasian Institute of Mining and Metallurgy Publication Series, 201–206. Perth, Australia: Australasian Institute of Mining and Metallurgy.

    Google Scholar 

  259. Dimitrakopoulos, R., and S.A. Abdel Sabour. 2007. Evaluating mine plans under uncertainty: Can the real options make a difference? Resources Policy 32 (3): 116–125. https://doi.org/10.1016/j.resourpol.2007.06.003.

    Article  Google Scholar 

  260. Abdel Sabour, S.A., R.G. Dimitrakopoulos, and M. Kumral. 2008. Mine design selection under uncertainty. Mining Technology 117 (2): 53–64. https://doi.org/10.1179/174328608X343065.

    Article  Google Scholar 

  261. Boland, N., I. Dumitrescu, and G. Froyland. 2008. A multistage stochastic programming approach to open-pit mine production scheduling with uncertain geology. http://www.optimization-online.org/DB_HTML/2008/10/2123.html. Accessed January 23, 2021.

  262. Albor Consuegra, F.R., and R. Dimitrakopoulos. 2009. Stochastic mine design optimization based on simulated annealing: Pit limits, production schedules, multiple orebody scenarios, and sensitivity analysis. Mining Technology 118 (2): 79–90. https://doi.org/10.1179/037178409X12541250836860.

    Article  Google Scholar 

  263. Meagher, C., S.A. Abdel Sabour, and R. Dimitrakopoulos. 2009. Pushback design of open-pit mines under geological and market uncertainties. In Proceedings of the International Symposium on Orebody Modelling and Strategic Mine Planning: Uncertainty and Risk Management, Perth.

    Google Scholar 

  264. Albor Consuegra, F.R., and R. Dimitrakopoulos. 2010. Algorithmic approaches to pushback design based on stochastic programming: Method, application, and comparisons. Mining Technology 119 (2): 88–101. https://doi.org/10.1179/037178410X12780655704761.

    Article  Google Scholar 

  265. Lagos, G., D. Espinoza, E. Moreno, and J. Amaya. 2011. Robust planning for an open-pit mining problem under ore-grade uncertainty. Electron Notes in Discrete Mathematics 37: 15–20. https://doi.org/10.1016/j.endm.2011.05.004.

    Article  MathSciNet  MATH  Google Scholar 

  266. Godoy, M., and R. Dimitrakopoulos. 2011. A risk quantification framework for strategic mine planning: Method and application. Journal of Mining Science 47 (2): 235–246. https://doi.org/10.1134/S1062739147020109.

    Article  Google Scholar 

  267. Dimitrakopoulos, R. 2011. Stochastic optimization for strategic mine planning: A decade of developments. Journal of Mining Science 47 (2): 138–150. https://doi.org/10.1134/S1062739147020018.

    Article  Google Scholar 

  268. Asad, M.W.A., and R. Dimitrakopoulos. 2013. Implementing a parametric maximum flow algorithm for optimal open pit mine design under uncertain supply and demand. The Journal of the Operational Research Society 64 (2): 185–197. https://doi.org/10.1057/jors.2012.26.

    Article  Google Scholar 

  269. Tachefine, B., and F. Soumis. 1997. Maximal closure on a graph with resource constraints. Computers and Operations Research 24 (10): 981–990. https://doi.org/10.1016/S0305-0548(97)00008-7.

    Article  MathSciNet  MATH  Google Scholar 

  270. Asad, M.W.A., and R. Dimitrakopoulos. 2012. Performance evaluation of a new stochastic network flow approach to optimal open pit mine design—Application at a gold mine. Journal of the South African Institute of Mining and Metallurgy 112 (7): 649–655.

    Google Scholar 

  271. Asad, M.W.A., and R. Dimitrakopoulos. 2012. Optimal production scale of open-pit mining operations with uncertain metal supply and long-term stockpiles. Resources Policy 37 (1): 81–89. https://doi.org/10.1016/j.resourpol.2011.12.002.

    Article  Google Scholar 

  272. Lamghari, A., and R. Dimitrakopoulos. 2012. A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty. European Journal of Operational Research 222 (3): 642–652. https://doi.org/10.1016/j.ejor.2012.05.029.

    Article  MATH  Google Scholar 

  273. Lamghari, A., and R. Dimitrakopoulos. 2016. Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty. European Journal of Operational Research 250: 273–290. https://doi.org/10.1016/j.ejor.2015.08.051.

    Article  MathSciNet  MATH  Google Scholar 

  274. Marcotte, D., and J. Caron. 2013. Ultimate open-pit stochastic optimization. Computers and Geosciences 51: 238–246. https://doi.org/10.1016/j.cageo.2012.08.008.

    Article  Google Scholar 

  275. Dimitrakopoulos, R., and A. Jewbali. 2013. Joint stochastic optimization of short and long term mine production planning: Method and application in a large operating gold mine. Mining Technology 122 (2): 110–123. https://doi.org/10.1179/1743286313y.0000000040.

    Article  Google Scholar 

  276. Benndorf, J., and R. Dimitrakopoulos. 2013. Stochastic long-term production scheduling of iron ore deposits: Integrating joint multi-element geological uncertainty. Journal of Mining Science 49 (1): 68–81. https://doi.org/10.1134/S1062739149010097.

    Article  Google Scholar 

  277. Goodfellow, R., and R. Dimitrakopoulos. 2013. Algorithmic integration of geological uncertainty in pushback designs for complex multiprocess open-pit mines. Mining Technology 122 (2): 67–77. https://doi.org/10.1179/147490013x13639459465736.

    Article  Google Scholar 

  278. Montiel, L., and R. Dimitrakopoulos. 2013. Stochastic mine production scheduling with multiple processes: Application at Escondida Norte, Chile. Journal of Mining Science 49: 583–597. https://doi.org/10.1134/S1062739149040096.

    Article  Google Scholar 

  279. Lamghari, A., R. Dimitrakopoulos, and J.A. Ferland. 2014. A variable neighborhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty. The Journal of the Operational Research Society 65 (9): 1305–1314. https://doi.org/10.1057/jors.2013.81.

    Article  Google Scholar 

  280. Koushavand, B., H. Askari-Nasab, and C.V. Deutsch. 2014. A linear programming model for long-term mine planning in the presence of grade uncertainty and a stockpile. International Journal of Mining Science and Technology 24 (4): 451–459. https://doi.org/10.1016/j.ijmst.2014.05.006.

    Article  Google Scholar 

  281. Del Castillo, F., and R. Dimitrakopoulos. 2014. Joint effect of commodity price and geological uncertainty over mine life and ultimate pit limit. Mining Technology 123 (4): 207–219. https://doi.org/10.1179/1743286314y.0000000069.

    Article  Google Scholar 

  282. Moosavi, E., and J. Gholamnejad. 2015. Long-term production scheduling modeling for the open-pit mines considering tonnage uncertainty via indicator kriging. Journal of Mining Science 51 (6): 1226–1234. https://doi.org/10.1134/S1062739115060526.

    Article  Google Scholar 

  283. de Freitas, S.M., R. Dimitrakopoulos, and A. Lamghari. 2015. Solving a large SIP model for production scheduling at a gold mine with multiple processing streams and uncertain geology. Mining Technology 124 (1): 24–33. https://doi.org/10.1179/1743286314y.0000000075.

  284. Montiel, L., and R. Dimitrakopoulos. 2015. Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach. European Journal of Operational Research 247 (1): 166–178. https://doi.org/10.1016/j.ejor.2015.05.002.

    Article  MATH  Google Scholar 

  285. Montiel, L., R. Dimitrakopoulos, and K. Kawahata. 2016. Globally optimizing open-pit and underground mining operations under geological uncertainty. Mining Technology 125 (1): 2–14. https://doi.org/10.1179/1743286315Y.0000000027.

    Article  Google Scholar 

  286. Del Castillo, M.F., M.C. Godoy, and R. Dimitrakopoulos. 2015. Optimal mining rates revisited: Managing mining equipment and geological risk at a given mine setup. Journal of Mining Science 51 (4): 785–798. https://doi.org/10.1134/S1062739115040165.

    Article  Google Scholar 

  287. Del Castillo, M.F., and R. Dimitrakopoulos. 2016. A multivariate destination policy for geometallurgical variables in mineral value chains using coalition-formation clustering. Resources Policy 50: 322–332. https://doi.org/10.1016/j.resourpol.2016.10.003.

    Article  Google Scholar 

  288. Gilani, S.O., and J. Sattarvand. 2016. Integrating geological uncertainty in long-term open pit mine production planning by ant colony optimization. Computers and Geosciences 87: 31–40. https://doi.org/10.1016/j.cageo.2015.11.008.

    Article  Google Scholar 

  289. Lamghari, A., and R. Dimitrakopoulos. 2016. Progressive hedging applied as a metaheuristic to schedule production in open-pit mines accounting for reserve uncertainty. European Journal of Operational Research 253 (3): 843–855. https://doi.org/10.1016/j.ejor.2016.03.007.

    Article  MathSciNet  MATH  Google Scholar 

  290. Matamoros, M.E.V., and R. Dimitrakopoulos. 2016. Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations, and blending restrictions. European Journal of Operational Research 255 (3): 911–921. https://doi.org/10.1016/j.ejor.2016.05.050.

    Article  MathSciNet  MATH  Google Scholar 

  291. Goodfellow, R.C., and R. Dimitrakopoulos. 2016. Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing 40: 292–304. https://doi.org/10.1016/j.asoc.2015.11.038.

    Article  Google Scholar 

  292. Goodfellow, R.C., and R. Dimitrakopoulos. 2017. Simultaneous stochastic optimization of mining complexes and mineral value chains. Mathematical Geosciences 49 (3): 341–360. https://doi.org/10.1007/s11004-017-9680-3.

    Article  Google Scholar 

  293. Montiel, L., and R. Dimitrakopoulos. 2017. A heuristic approach for the stochastic optimization of mine production schedules. Journal of Heuristics 23: 397–415. https://doi.org/10.1007/s10732-017-9349-6.

    Article  Google Scholar 

  294. Montiel, L., and R. Dimitrakopoulos. 2018. Simultaneous stochastic optimization of production scheduling at Twin Creeks Mining Complex, Nevada. Mining Engineering 70 (12): 48–56. https://doi.org/10.19150/me.8645.

  295. Moreno, E., X. Emery, M. Goycoolea, N. Morales, and G. Nelis. 2017. A two-stage stochastic model for open pit mine planning under geological uncertainty. In Proceedings of the 38h International APCOM Symposium, Golden.

    Google Scholar 

  296. Richmond, A. 2018. Direct net present value open pit optimization with probabilistic models. In Advances in Applied Strategic Mine Planning, ed. R. Dimitrakopoulos, 217–228. Cham: Springer. https://doi.org/10.1007/978-3-319-69320-0_15.

  297. Jamshidi, M., and M. Osanloo. 2018. UPL determination of multi-element deposits with grade uncertainty using a new block economic value calculation approach. Journal of Mining and Environment 9 (1): 61–72. https://doi.org/10.22044/jme.2017.5763.1387.

    Article  Google Scholar 

  298. Rimélé, M.A., R. Dimitrakopoulos, and M. Gamache. 2018. A stochastic optimization method with in-pit waste and tailings disposal for open pit life-of-mine production planning. Resources Policy 57: 112–121. https://doi.org/10.1016/j.resourpol.2018.02.006.

    Article  Google Scholar 

  299. Ajak, A.D., E. Lilford, and E. Topal. 2018. Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty. Resources Policy 55: 62–79. https://doi.org/10.1016/j.resourpol.2017.10.016.

    Article  Google Scholar 

  300. Quigley, M., R. Dimitrakopoulos, and T. Grammatikopoulos. 2018. Risk-resilient mine production schedules with good quality for rare earth element projects. Mining Technology 127 (1): 41–55. https://doi.org/10.1080/14749009.2017.1323172.

    Article  Google Scholar 

  301. Paithankar, A., and S. Chatterjee. 2019. Open-pit mine production schedule optimization using a maximum-flow and genetic algorithms hybrid. Applied Soft Computing 81. https://doi.org/10.1016/j.asoc.2019.105507.

  302. Del Castillo, M.F., and R. Dimitrakopoulos. 2019. Dynamically optimizing the strategic plan of mining complexes under supply uncertainty. Resources Policy 60: 83–93. https://doi.org/10.1016/j.resourpol.2018.11.019.

    Article  Google Scholar 

  303. Mai, N.L., E. Topal, O. Erten, and B. Sommerville. 2019. A new risk-based optimization method for the iron ore production scheduling using stochastic integer programming. Resources Policy 62: 571–579. https://doi.org/10.1016/j.resourpol.2018.11.004.

    Article  Google Scholar 

  304. Saliba, Z., and R. Dimitrakopoulos. 2019. Simultaneous stochastic optimization of an open-pit gold mining complex with supply and market uncertainty. Mining Technology 128 (4): 216–229. https://doi.org/10.1080/25726668.2019.1626169.

    Article  Google Scholar 

  305. Sepulveda, G.F., P.J. Alvarez, and J.B. Bedoya. 2020. Stochastic optimization in mine planning schedule. Computers and Operations Research 115. https://doi.org/10.1016/j.cor.2019.104823.

  306. Quigley, M., and R. Dimitrakopoulos. 2020. Incorporating geological and equipment performance uncertainty while optimizing short-term mine production schedules. International Journal of Mining, Reclamation and Environment 34 (5): 362–383. https://doi.org/10.1080/17480930.2019.1658923.

    Article  Google Scholar 

  307. Lamghari, A., and R. Dimitrakopoulos. 2020. Hyper-heuristic approaches for strategic mine planning under uncertainty. Computers and Operations Research 115. https://doi.org/10.1016/j.cor.2018.11.010.

  308. Lagos, T., M. Armstrong, T. Homem-de-Mello, G. Lagos, and D. Sauré. 2020. A framework for adaptive open-pit mining planning under geological uncertainty. Optimization and Engineering. https://doi.org/10.1007/s11081-020-09557-0.

    Article  Google Scholar 

  309. Gilani, S.O., J. Sattarvand, M. Hajihassani, and S.S. Abdullah. 2020. A stochastic particle swarm-based model for long term production planning of open-pit mines considering the geological uncertainty. Resources Policy 68. https://doi.org/10.1016/j.resourpol.2020.101738.

  310. Both, C., and R. Dimitrakopoulos. 2020. Joint stochastic short-term production scheduling and fleet management optimization for mining complexes. Optimization and Engineering 21: 1717–1743. https://doi.org/10.1007/s11081-020-09495-x.

    Article  MathSciNet  Google Scholar 

  311. Dimitrakopoulos, R., and R. Senécal. 2020. Long-term mine production scheduling with multiple processing destinations under mineral supply uncertainty, based on multi-neighborhood Tabu search. International Journal of Mining, Reclamation and Environment 34 (7): 459–475. https://doi.org/10.1080/17480930.2019.1595902.

    Article  Google Scholar 

  312. Maleki, M., E. Jélvez, X. Emery, and N. Morales. 2020. Stochastic open-pit mine production scheduling: An iron deposit case study. Minerals 10 (7): 585. https://doi.org/10.3390/min10070585.

    Article  Google Scholar 

  313. Rimélé, A., R. Dimitrakopoulos, and M. Gamache. 2020. A dynamic stochastic programming approach for open-pit mine planning with geological and commodity price uncertainty. Resources Policy 65. https://doi.org/10.1016/j.resourpol.2019.101570.

  314. Chatterjee, S., and R. Dimitrakopoulos. 2020. Production scheduling under uncertainty of an open-pit mine using Lagrangian relaxation and branch-and-cut algorithm. International Journal of Mining, Reclamation and Environment 34 (5): 343–361. https://doi.org/10.1080/17480930.2019.1631427.

    Article  Google Scholar 

  315. Tolouei, K., E. Moosavi, A.H. Bangin Tabrizi, P. Afzal, and A.A. Aghajani Bazzazi. 2020. A comprehensive study of several meta-heuristic algorithms for open-pit mine production scheduling problem considering grade uncertainty. Journal of Mining and Environment 11 (3): 721–736. https://doi.org/10.22044/jme.2020.9127.1803.

    Article  Google Scholar 

  316. Tolouei, K., E. Moosavi, A.H.B. Tabrizi, and P. Afzal. 2021. Application of an improved Lagrangian relaxation approach in the constrained long-term production scheduling problem under grade uncertainty. Engineering Optimization 53 (5): 735–753. https://doi.org/10.1080/0305215X.2020.1746295.

    Article  MathSciNet  Google Scholar 

  317. Humphrays, D. 2019. Mining productivity and the fourth industrial revolution. Mineral Economics 33: 115–125. https://doi.org/10.1007/s13563-019-00172-9.

    Article  Google Scholar 

  318. Bassan, J., V. Srinivasan, P. Knights, and C. Farrelly. 2008. A day in the life of a mine worker in 2025. In Proceedings of the First International Future Mining Conference, Sydney.

    Google Scholar 

  319. Guieiro, G.A., E.A. Leitão, E.S. Balbino, and L. van Melis. 2019. Implantação de caminhões fora de estrada autônomos na indústria de mineração. In Proceedings of 20° Simpósio de Mineração, São Paulo. https://doi.org/10.5151/2594-357X-33680.

  320. Bassan, J., and P. Knights. 2008. Applications of advanced analytics in mining: Safer, smarter, sustainable operations. In Proceedings of the Australian Mining Technology Conference, Queensland.

    Google Scholar 

  321. Rupp, K. 2018. 42 years of microprocessor trend data. https://www.karlrupp.net/2018/02/42-years-of-microprocessor-trend-data. Accessed September 29, 2018.

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Mariz, J.L.V., Soofastaei, A. (2022). Advanced Analytics for Surface Mine Planning. In: Soofastaei, A. (eds) Advanced Analytics in Mining Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91589-6_9

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