Machine learning for open-pit mining: a systematic review

References

• Trading Economics, Eur. Union Min. Prod. (2022). https://tradingeconomics.com/european-union/mining-production (Accessed: October 24, 2022).
   Google Scholar
• Trading Economics, GDP from Min. | Eur. (2022). https://tradingeconomics.com/country-list/gdp-from-mining?continent=europe (Accessed: October 24, 2022).
   Google Scholar
• X. Peng, R. Shoumai, Global Mining Development Report 2020-2021, 2021
   Google Scholar
• Y. Babets, Y. Terekhov, T. Mormul, I. Melnikova, V. Komirna, Environmental and economic assessment of the possibilities to increase the land preservation level in terms of open-pit mining, E3S Web Conf. 280 (2021) 11022. 10.1051/e3sconf/202128011002.
   Google Scholar
• R. V. Ramani, Surface mining technology: Progress and prospects, Procedia Eng. 46 (2012) 9–21. 10.1016/j.proeng.2012.09.440.
   Google Scholar
• C. Mestanza-Ramón, J. Cuenca-Cumbicus, G. D’orio, J. Flores-Toala, S. Segovia-Cáceres, A. Bonilla-Bonilla, S. Straface, Gold mining in the Amazon region of ecuador: History and a review of its socio-environmental impacts, Land 11 (2022) 1–22. 10.3390/land11020221.
   PubMed Web of Science ®Google Scholar
• A.L. Samuel, Eight-move opening utilizing generalization learning. (See Appendix B, Game G-43.1 Some Studies in Machine Learning Using the Game of Checkers, IBM J. (1959) 210–229. 10.1147/rd.33.0210.
   Google Scholar
• A.S. Rathore, S. Nikita, G. Thakur, S. Mishra, Artificial intelligence and machine learning applications in biopharmaceutical manufacturing, Trends Biotechnol. (2022) 1–14. 10.1016/j.tibtech.2022.08.007.
   Web of Science ®Google Scholar
• M. Duquesnoy, C. Liu, D.Z. Dominguez, V. Kumar, E. Ayerbe, A.A. Franco, Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations, Energy Storage Mater. 56 (2023) 50–61. 10.1016/j.ensm.2022.12.040.
   Web of Science ®Google Scholar
• S. Feng, H. Sun, X. Yan, H. Zhu, Z. Zou, S. Shen, Dense reinforcement learning for safety validation of autonomous vehicles, Nature 615 (2023) 620–627. 10.1038/s41586-023-05732-2.
   PubMed Web of Science ®Google Scholar
• Y. Liu, X. Zuo, G. Ai, Y. Liu, A reinforcement learning-based approach for online bus scheduling, Knowledge-Based Syst. 271 (2023) 110584. 10.1016/j.knosys.2023.110584.
   Web of Science ®Google Scholar
• W. Pan, S.Q. Liu, Deep reinforcement learning for the dynamic and uncertain vehicle routing problem, Appl. Intell. 53 (2023) 405–422. 10.1007/s10489-022-03456-w.
   Web of Science ®Google Scholar
• Y. Ketkar, S. Gawade, A decision support system for selecting the most suitable machine learning in healthcare using user parameters and requirements, Healthc. Anal. 2 (2022) 100117. 10.1016/j.health.2022.100117.
   Google Scholar
• M. Zini, C. Carcasci, Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy, Energy 262 (2023) 125576. 10.1016/j.energy.2022.125576.
   Web of Science ®Google Scholar
• S. Kim, S. Lee, Nonlinear relationships and interaction effects of an urban environment on crime incidence : Application of urban big data and an interpretable machine learning method, Sustain. Cities Soc. 91 (2023) 104419. 10.1016/j.scs.2023.104419.
   Web of Science ®Google Scholar
• X. Zhang, L. Yang, R. Luo, H.Y. Wu, J. Xu, C. Huang, Y. Ruan, X. Zheng, J. Yao, Estimating the outdoor environment of workers’ villages in East China using machine learning, Build. Environ. 226 (2022) 109738. 10.1016/j.buildenv.2022.109738.
   Web of Science ®Google Scholar
• D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis, A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play, Science (80-.). 362 (2018) 1140–1144. 10.1126/science.aar6404.
   Web of Science ®Google Scholar
• M.R. Dobbelaere, P.P. Plehiers, R. Van de Vijver, C. V. Stevens, K.M. Van Geem, Machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats, Engineering 7 (2021) 1201–1211. 10.1016/j.eng.2021.03.019.
   Web of Science ®Google Scholar
• A. Thebelt, J. Wiebe, J. Kronqvist, C. Tsay, R. Misener, Maximizing information from chemical engineering data sets: Applications to machine learning, Chem. Eng. Sci. 252 (2022) 117469. 10.1016/j.ces.2022.117469.
   Web of Science ®Google Scholar
• R. Aworka, L.S. Cedric, W.Y.H. Adoni, J.T. Zoueu, F.K. Mutombo, C.L.M. Kimpolo, T. Nahhal, M. Krichen, Agricultural decision system based on advanced machine learning models for yield prediction: Case of East African countries, Smart Agric. Technol. 2 (2022) 100048. 10.1016/j.atech.2022.100048.
   Google Scholar
• G. Bai, T. Xu, Coal mine safety evaluation based on machine learning: A BP neural network model, Comput. Intell. Neurosci. 2022(2022) 1–9. 10.1155/2022/5233845.
   Google Scholar
• T. D’Angelo, M. Mendes, B. Keller, R. Ferreira, S. Delabrida, R. Rabelo, H. Azpurua, A. Bianchi, Deep learning-based object detection for digital inspection in the mining industry, Proc. - 18th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2019 (2019) 633–640. 10.1109/ICMLA.2019.00116.
   Google Scholar
• B. Mahesh, Machine learning algorithms - A review, Int. J. Sci. Res. 9 (2020) 381–386. 10.21275/ART20203995.
   Google Scholar
• R. Noriega, Y. Pourrahimian, A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning, Resour. Policy 77 (2022) 102727. 10.1016/j.resourpol.2022.102727.
   Google Scholar
• M. Naeem, S.T.H. Rizvi, A. Coronato, A gentle introduction to reinforcement learning and its application in different fields, IEEE Access 8 (2020) 209320–209344. 10.1109/ACCESS.2020.3038605.
   Web of Science ®Google Scholar
• W.A. Khan, S.H. Chung, H.L. Ma, S.Q. Liu, C.Y. Chan, A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption, Transp. Res. Part E 132 (2019) 72–96. 10.1016/j.tre.2019.10.005.
   Google Scholar
• D. Zhang, X. Han, C. Deng, Review on the research and practice of deep learning and reinforcement learning in smart grids, CSEE J. Power Energy Syst. 4 (2018) 362–370. 10.17775/cseejpes.2018.00520.
   Web of Science ®Google Scholar
• P. Hernandez-Leal, B. Kartal, M.E. Taylor, A survey and critique of multiagent deep reinforcement learning, Auton. Agent. Multi. Agent. Syst. 33 (2019) 750–797. 10.1007/s10458-019-09421-1.
   Web of Science ®Google Scholar
• J.L.V. Mariz, M.M. Badiozamani, L. de Peroni, R.M. de A. Silva, A critical review of bench aggregation and mining cut clustering techniques based on optimization and artificial intelligence to enhance the open-pit mine planning, Eng. Appl. Artif. Intell. 133 (2024) 108334. 10.1016/j.engappai.2024.108334.
   Web of Science ®Google Scholar
• P. Chimunhu, E. Topal, A.D. Ajak, W. Asad, A review of machine learning applications for underground mine planning and scheduling, Resour. Policy 77 (2022) 102693. 10.1016/j.resourpol.2022.102693.
   Google Scholar
• D. Jung, Y. Choi, Systematic review of machine learning applications in mining: Exploration, exploitation, and reclamation, Minerals 11 (2021) 148. 10.3390/min11020148.
   Web of Science ®Google Scholar
• N. Parvez Farazi, B. Zou, T. Ahamed, L. Barua, Deep reinforcement learning in transportation research: A review, Transp. Res. Interdiscip. Perspect. 11 (2021) 100425. 10.1016/j.trip.2021.100425.
   Google Scholar
• Y. Fu, C. Aldrich, Deep learning in mining and mineral processing operations: A review, IFAC-PapersOnLine 53 (2020) 11920–11925. 10.1016/j.ifacol.2020.12.712.
   Google Scholar
• J.T. McCoy, L. Auret, Machine learning applications in minerals processing: A review, Miner. Eng. 132 (2019) 95–109. 10.1016/j.mineng.2018.12.004.
   Google Scholar
• A. Salazar-Moya, M. V. Garcia, Lot streaming in different types of production processes: A prisma systematic review, Designs 5 (2021) 67. 10.3390/designs5040067.
   Google Scholar
• A. Kamel, M. Elwageeh, S. Bonduà, M. Elkarmoty, Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt, Resour. Policy 80 (2023) 103266. 10.1016/j.resourpol.2022.103266.
   Google Scholar
• K. Zhang, X. Yang, T. Wang, J. Thé, Z. Tan, H. Yu, Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms, J. Clean. Prod. 405 (2023) 136959. 10.1016/j.jclepro.2023.136959.
   Google Scholar
• T. Shi, C. Li, W. Zhang, Y. Zhang, Forecasting on metal resource spot settlement price: New evidence from the machine learning model, Resour. Policy 81 (2023) 103360. 10.1016/j.resourpol.2023.103360.
   Google Scholar
• J. Zhao, S. Hosseini, Q. Chen, D. Jahed Armaghani, Super learner ensemble model: A novel approach for predicting monthly copper price in future, Resour. Policy 85 (2023) 103903. 10.1016/j.resourpol.2023.103903.
   Google Scholar
• S. Das, T.P. Sahu, R.R. Janghel, Oil and gold price prediction using optimized fuzzy inference system based extreme learning machine, Resour. Policy 79 (2022) 103109. 10.1016/j.resourpol.2022.103109.
   Google Scholar
• H. Zhang, H. Nguyen, D.A. Vu, X.N. Bui, B. Pradhan, Forecasting monthly copper price: A comparative study of various machine learning-based methods, Resour. Policy 73 (2021) 102189. 10.1016/j.resourpol.2021.102189.
   Google Scholar
• H. Zhang, H. Nguyen, X.N. Bui, B. Pradhan, N.L. Mai, D.A. Vu, Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms, Resour. Policy 73 (2021) 102195. 10.1016/j.resourpol.2021.102195.
   Google Scholar
• P. Du, J. Guo, S. Sun, S. Wang, J. Wu, Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm, Resour. Policy 74 (2021) 102335. 10.1016/j.resourpol.2021.102335.
   Google Scholar
• X. Zheng, H. Nguyen, X.N. Bui, Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model, Resour. Policy 74 (2021) 102300. 10.1016/j.resourpol.2021.102300.
   Google Scholar
• H. Guo, H. Nguyen, D.A. Vu, X.N. Bui, Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach. Resour Policy 74 (2021) 101474. 10.1016/j.resourpol.2019.101474.
   Web of Science ®Google Scholar
• H. Zhang, H. Nguyen, X.N. Bui, T. Nguyen-Thoi, T.T. Bui, N. Nguyen, D.A. Vu, V. Mahesh, H. Moayedi, Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm, Resour. Policy 66 (2020) 101604. 10.1016/j.resourpol.2020.101604.
   Google Scholar
• H. Nourali, M. Osanloo, A regression-tree-based model for mining capital cost estimation, Int. J. Mining, Reclam. Environ. 34 (2020) 88–100. 10.1080/17480930.2018.1510300.
   Web of Science ®Google Scholar
• H. Nourali, M. Osanloo, Mining capital cost estimation using Support Vector Regression (SVR), Resour. Policy 62 (2019) 527–540. 10.1016/j.resourpol.2018.10.008.
   Web of Science ®Google Scholar
• G. Franco-Sepulveda, C. Campuzano, C. Pineda, NPV risk simulation of an open pit gold mine project under the O’Hara cost model by using GAs, Int. J. Min. Sci. Technol. 27 (2017) 557–565. 10.1016/j.ijmst.2017.03.004.
   Web of Science ®Google Scholar
• E. Brenjkar, E.B. Delijani, Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models, J. Pet. Sci. Eng. 210 (2022) 110033. 10.1016/j.petrol.2021.110033.
   Web of Science ®Google Scholar
• A. Buelga Díaz, I. Diego Álvarez, C. Castañón Fernández, A. Krzemień, F.J. Iglesias Rodríguez, Calculating ultimate pit limits and determining pushbacks in open-pit mining projects, Resour. Policy 72 (2021) 102058. 10.1016/j.resourpol.2021.102058.
   Google Scholar
• D.S. Hochbaum, A. Chen, Performance analysis and best implementations of old and new algorithms for the open-pit mining problem, Oper. Res. 48 (2000) 894–914. 10.1287/opre.48.6.894.12392.
   Google Scholar
• G.T. Nwaila, S.E. Zhang, J.E. Bourdeau, H.E. Frimmel, Y. Ghorbani, Spatial interpolation using machine learning: from patterns and regularities to block models, Springer US, 2024. 10.1007/s11053-023-10280-7.
   Google Scholar
• S. Avalos, J.M. Ortiz, Multivariate Geostatistical Simulation and Deep Q-Learning to optimize mining decisions, Math. Geosci. 55 (2023) 673–692. 10.1007/s11004-023-10049-x.
   Web of Science ®Google Scholar
• J. Qin, S. Du, J. Ye, R. Yong, SVNN-ANFIS approach for stability evaluation of open-pit mine slopes, Expert Syst. Appl. 198 (2022) 116816. 10.1016/j.eswa.2022.116816.
   Web of Science ®Google Scholar
• J. Williams, J. Singh, M. Kumral, J. Ramirez Ruiseco, Exploring deep learning for dig-limit optimization in open-pit mines, Nat. Resour. Res. 30 (2021) 2085–2101. 10.1007/s11053-021-09864-y.
   Web of Science ®Google Scholar
• A. Quelopana, A. Navarra, Integration of strategic open-pit mine planning into hierarchical artificial intelligence, J. South. African Inst. Min. Metall. 121 (2021) 643–652. 10.17159/2411-9717/1367/2021.
   Google Scholar
• T.J. Mbah, H. Ye, J. Zhang, M. Long, Implementation of statistical analysis to determine optimum ultimate pit, Open J. Geol. 10 (2020) 1262–1279. 10.4236/ojg.2020.1012063.
   Google Scholar
• J. Zhou, E. Li, S. Yang, M. Wang, X. Shi, S. Yao, H.S. Mitri, Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories, Saf. Sci. 118 (2019) 505–518. 10.1016/j.ssci.2019.05.046.
   Web of Science ®Google Scholar
• A. Agosti, S. Utili, C. Valderrama, G. Albornoz, Optimal pitwall profiles to maximise the overall slope angle of open pit mines: the McLaughlin Mine, SSIM 2021 Second Int. Slope Stab. Min. (2021) 69–82. 10.36487/acg_repo/2135_01.
   Google Scholar
• N. Azadi, H. Mirzaei-Nasirabad, A. Mousavi, Evaluating the efficiency of the genetic algorithm in designing the ultimate pit limit of open-pit mines, Int. J. Min. Geo-Engineering 57 (2023) 55–58. 10.22059/ijmge.2022.340973.594963.
   Google Scholar
• F. Navarro, N. Morales, C. Contreras-Bolton, C. Rey, V. Parada, Open-pit pushback optimization by a parallel genetic algorithm, Minerals 14 (2024) 438. 10.3390/min14050438.
   Web of Science ®Google Scholar
• G. Turan, A.H. Onur, Optimization of open-pit mine design and production planning with an improved floating cone algorithm, Optim. Eng. 24 (2023) 1157–1181. 10.1007/s11081-022-09725-4.
   Web of Science ®Google Scholar
• H. Askari-Nasab, K. Awuah-Offei, An agent-based framework for open pit mine planning, CIM Bull. 118 (2008) 1–12.
   Google Scholar
• H. Askari-Nasab, K. Awuah-Offei, Open pit optimisation using discounted economic block values, Min. Technol. 118 (2009) 1–12. 10.1179/037178409X12450752943243.
   Google Scholar
• W.B. Lambert, A. Brickey, A.M. Newman, K. Eurek, Open-pit block-sequencing formulations: a tutorial. Interfaces (Providence) 44 (2014) 127–142
   Web of Science ®Google Scholar
• S.Q. Liu, E. Kozan, New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems, Expert Syst. Appl. 43 (2016) 59–65. 10.1016/j.eswa.2015.08.044.
   Web of Science ®Google Scholar
• D. Espinoza, M. Goycoolea, E. Moreno, A. Newman, MineLib:a library of open pit mining problems. Ann. Oper. Res. 206 (2013) 93–114. 10.1007/s10479-012-1258-3.
   Web of Science ®Google Scholar
• O.R. Letelier, D. Espinoza, M. Goycoolea, E. Moreno, G. Muñoz, Production scheduling for strategic open pit mine planning: A mixed-integer programming approach, Oper. Res. 68 (2020) 1425–1444. 10.1287/opre.2019.1965.
   Google Scholar
• A. Lamghari, R. Dimitrakopoulos, A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty, Eur. J. Oper. Res. 222 (2012) 642–652. 10.1016/j.ejor.2012.05.029.
   Web of Science ®Google Scholar
• M. Kumral, Robust stochastic mine production scheduling, Eng. Optim. 42 (2010) 567–579. 10.1080/03052150903353336.
   Web of Science ®Google Scholar
• R. Chicoisne, D. Espinoza, M. Goycoolea, E. Moreno, E. Rubio, A new algorithm for the open-pit mine production scheduling problem, Oper. Res. 60 (2012) 517–528. 10.1287/opre.1120.1050.
   Google Scholar
• A. Mousavi, E. Kozan, S.Q. Liu, Comparative analysis of three metaheuristics for short-term open pit block sequencing, J. Heuristics 22 (2016) 301–329. 10.1007/s10732-016-9311-z.
   Web of Science ®Google Scholar
• T.W.M. Vossen, R.K. Wood, A.M. Newman, Hierarchical benders decomposition for open-pit mine block sequencing, Oper. Res. 64 (2016) 771–793. 10.1287/opre.2016.1516.
   Google Scholar
• E. Jélvez, N. Morales, P. Nancel-Penard, F. Cornillier, A new hybrid heuristic algorithm for the Precedence Constrained Production Scheduling Problem: A mining application, Omega 94 (2020) 102046. 10.1016/j.omega.2019.03.004.
   Web of Science ®Google Scholar
• A. Mousavi, E. Kozan, S.Q. Liu, Open-pit block sequencing optimization: A mathematical model and solution technique, Eng. Optim. 48 (2016) 1932–1950. 10.1080/0305215X.2016.1142080.
   Web of Science ®Google Scholar
• R. Noriega, Y. Pourrahimian, Shovel allocation and scheduling for open-pit mining using deep reinforcement learning, Int. J. Mining, Reclam. Environ. 00 (2024) 1–18. 10.1080/17480930.2024.2323325.
   Google Scholar
• Z. Levinson, R. Dimitrakopoulos, J. Keutchayan, Simultaneous stochastic optimization of an open-pit mining complex with preconcentration using reinforcement learning, Appl. Soft Comput. 138 (2023) 110180. 10.1016/j.asoc.2023.110180.
   Web of Science ®Google Scholar
• Z. Feng, G. Liu, L. Wang, Q. Gu, L. Chen, Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning, Sustainability 15 (2023) 5279.
   Web of Science ®Google Scholar
• C. Both, R. Dimitrakopoulos, Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes, Int. J. Min. Sci. Technol. 33 (2022) 185–199. 10.1016/j.ijmst.2022.10.001.
   Web of Science ®Google Scholar
• A. Kumar, R. Dimitrakopoulos, Production scheduling in industrial mining complexes with incoming new information using tree search and deep reinforcement learning, Appl. Soft Comput. 110 (2021) 107644. 10.1016/j.asoc.2021.107644.
   Web of Science ®Google Scholar
• A. Kumar, R. Dimitrakopoulos, M. Maulen, Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex, J. Intell. Manuf. 31 (2020) 1795–1811. 10.1007/s10845-020-01562-5.
   Web of Science ®Google Scholar
• C. Paduraru, R. Dimitrakopoulos, Adaptive policies for short-term material flow optimization in a mining complex, Min. Technol. Trans. Inst. Min. Metall. 127 (2018) 56–63. 10.1080/14749009.2017.1341142.
   Web of Science ®Google Scholar
• C. Paduraru, R. Dimitrakopoulos, Responding to new information in a mining complex: fast mechanisms using machine learning, Min. Technol. Trans. Inst. Min. Metall. 128 (2019) 129–142. 10.1080/25726668.2019.1577596.
   Web of Science ®Google Scholar
• T. Wambeke, J. Benndorf, A simulation-based geostatistical approach to real-time reconciliation of the grade control model, Math. Geosci. 49 (2017) 1–37. 10.1007/s11004-016-9658-6.
   Web of Science ®Google Scholar
• P. Cosmin, R. Dimitrakopoulos, Mineral supply chain optimization under uncertainty using approximate dynamic programming, Math. Sci. Eng. 40 (2015) 101–137. 10.1016/S0076-5392(08)61063-2.
   Google Scholar
• M. Tabesh, C. Mieth, H. Askari-Nasab, A multi-step approach to long-term open-pit production planning, Int. J. Min. Miner. Eng. 5 (2014) 273–298. 10.1504/IJMME.2014.066577.
   Google Scholar
• A. Lamghari, R. Dimitrakopoulos, Hyper-heuristic approaches for strategic mine planning under uncertainty, Comput. Oper. Res. 115 (2020) 104590. 10.1016/j.cor.2018.11.010.
   Web of Science ®Google Scholar
• R. Dimitrakopoulos, M. Godoy, Grade control based on economic ore/waste classification functions and stochastic simulations: Examples, comparisons and applications, Trans. Institutions Min. Metall. Sect. A Min. Technol. 123 (2014) 90–106. 10.1179/1743286314Y.0000000062.
   Google Scholar
• N. Li, D. Liu, L. Wang, H. Ye, Q. Wang, D. Yan, S. Zhao, Combination prediction of underground mine rock drilling time based on seasonal and trend decomposition using Loess, Eng. Appl. Artif. Intell. 133 (2024) 108064. 10.1016/j.engappai.2024.108064.
   Google Scholar
• C. Zhang, X. Song, Z. Liu, B. Ma, Z. Lv, Y. Su, G. Li, Z. Zhu, Real-time and multi-objective optimization of rate-of-penetration using machine learning methods, Geoenergy Sci. Eng. 223 (2023) 211568. 10.1016/j.geoen.2023.211568.
   Google Scholar
• E. Gurina, N. Klyuchnikov, K. Antipova, D. Koroteev, Making the black-box brighter: Interpreting machine learning algorithm for forecasting drilling accidents, J. Pet. Sci. Eng. 218 (2022) 111041. 10.1016/j.petrol.2022.111041.
   Web of Science ®Google Scholar
• L. Liu, W. Cao, H. Liu, A. Ord, Y. Qin, F. Zhou, C. Bi, Applying benefits and avoiding pitfalls of 3D computational modeling-based machine learning prediction for exploration targeting: Lessons from two mines in the Tongling-Anqing district, eastern China, Ore Geol. Rev. 142 (2022) 104712. 10.1016/j.oregeorev.2022.104712.
   Web of Science ®Google Scholar
• Y. Zhou, X. Chen, H. Zhao, M. Wu, W. Cao, Y. Zhang, H. Liu, A novel rate of penetration prediction model with identified condition for the complex geological drilling process, J. Process Control 100 (2021) 30–40. 10.1016/j.jprocont.2021.02.001.
   Web of Science ®Google Scholar
• A.T. Tunkiel, D. Sui, T. Wiktorski, Training-while-drilling approach to inclination prediction in directional drilling utilizing recurrent neural networks, J. Pet. Sci. Eng. 196 (2021) 108128. 10.1016/j.petrol.2020.108128.
   Web of Science ®Google Scholar
• H. Fan, M. Wu, W. Cao, X. Lai, L. Chen, C. Lu, S. Du, J. She, An operating performance assessment strategy with multiple modes based on least squares support vector machines for drilling process, Comput. Ind. Eng. 159 (2021) 107492. 10.1016/j.cie.2021.107492.
   Web of Science ®Google Scholar
• E. Gurina, N. Klyuchnikov, A. Zaytsev, E. Romanenkova, K. Antipova, I. Simon, V. Makarov, D. Koroteev, Application of machine learning to accidents detection at directional drilling, J. Pet. Sci. Eng. 184 (2020) 106519. 10.1016/j.petrol.2019.106519.
   Web of Science ®Google Scholar
• M. Oyedere, K. Gray, ROP and TOB optimization using machine learning classification algorithms, J. Nat. Gas Sci. Eng. 77 (2020) 103230. 10.1016/j.jngse.2020.103230.
   Web of Science ®Google Scholar
• M. Oyedere, K. Gray, Torque-on-bit (TOB) prediction and optimization using machine learning algorithms, J. Nat. Gas Sci. Eng. 84 (2020) 103623. 10.1016/j.jngse.2020.103623.
   Web of Science ®Google Scholar
• R. Dirkx, R. Dimitrakopoulos, Optimizing infill drilling decisions using multi-armed bandits: Application in a long-term, multi-element stockpile, Math. Geosci. 50 (2018) 35–52. 10.1007/s11004-017-9695-9.
   PubMed Web of Science ®Google Scholar
• E. Bakhtavar, S. Yousefi, Analysis of ground vibration risk on mine infrastructures: integrating fuzzy slack-based measure model and failure effects analysis, Int. J. Environ. Sci. Technol. 16 (2019) 6065–6076. 10.1007/s13762-018-2008-0.
   Web of Science ®Google Scholar
• V. Mwango Bowa, Optimization of blasting design parameters on open pit bench a case study of Nchanga open pits, Int. J. Sci. Technol. Res. 4 (2015) 45–51. www.ijstr.org.
   Google Scholar
• J. Zhou, Y. Zhang, Y. Qiu, State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting, Artif. Intell. Rev. 57 (2024) 1–54. 10.1007/s10462-023-10636-8.
   Web of Science ®Google Scholar
• S. Hosseini, R. Pourmirzaee, Green policy for managing blasting induced dust dispersion in open-pit mines using probability-based deep learning algorithm, Expert Syst. Appl. 240 (2024) 122469. 10.1016/j.eswa.2023.122469.
   Web of Science ®Google Scholar
• X. Wang, S. Hosseini, D. Jahed Armaghani, E. Tonnizam Mohamad, Data-driven optimized artificial neural network technique for prediction of flyrock induced by boulder blasting, Mathematics 11 (2023) 1–22. 10.3390/math11102358.
   Web of Science ®Google Scholar
• S. Hosseini, R. Pourmirzaee, D.J. Armaghani, M.M. Sabri Sabri, Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques, Sci. Rep. 13 (2023) 1–20. 10.1038/s41598-023-33796-7.
   Google Scholar
• J. Ye, X. He, A novel hybrid of ANFIS-based models using optimisation approaches to predict mine blast-induced flyrock, Int. J. Environ. Sci. Technol. 20 (2023) 3673–3686. 10.1007/s13762-022-04170-3.
   Web of Science ®Google Scholar
• H. Nguyen, X.N. Bui, E. Topal, Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings, Reliab. Eng. Syst. Saf. 231 (2023) 109032. 10.1016/j.ress.2022.109032.
   Web of Science ®Google Scholar
• J. Zhou, Y. Qiu, M. Khandelwal, S. Zhu, X. Zhang, Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations, Int. J. Rock Mech. Min. Sci. 145 (2021) 104856. 10.1016/j.ijrmms.2021.104856.
   Web of Science ®Google Scholar
• C. Xie, H. Nguyen, X.N. Bui, Y. Choi, J. Zhou, T. Nguyen-Trang, Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms, Geosci. Front. 12 (2021) 101108. 10.1016/j.gsf.2020.11.005.
   Google Scholar
• N.K. Bhagat, A. Rana, A.K. Mishra, M.M. Singh, A. Singh, P.K. Singh, Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique, Geomatics, Nat. Hazards Risk 12 (2021) 1715–1740. 10.1080/19475705.2021.1944917.
   Web of Science ®Google Scholar
• B.R. Murlidhar, H. Nguyen, J. Rostami, X.N. Bui, D.J. Armaghani, P. Ragam, E.T. Mohamad, Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network, J. Rock Mech. Geotech. Eng. 13 (2021) 1413–1427. 10.1016/j.jrmge.2021.08.005.
   Web of Science ®Google Scholar
• X. Lu, M. Hasanipanah, K. Brindhadevi, H. Bakhshandeh Amnieh, S. Khalafi, ORELM: A novel machine learning approach for prediction of flyrock in mine blasting, Nat. Resour. Res. 29 (2020) 641–654. 10.1007/s11053-019-09532-2.
   Web of Science ®Google Scholar
• H.N. Rad, I. Bakhshayeshi, W.A. Wan Jusoh, M.M. Tahir, L.K. Foong, Prediction of flyrock in mine blasting: A new computational intelligence approach, Nat. Resour. Res. 29 (2020) 609–623. 10.1007/s11053-019-09464-x.
   Web of Science ®Google Scholar
• Q. Fang, H. Nguyen, X.N. Bui, T. Nguyen-Thoi, Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules, Nat. Resour. Res. 29 (2020) 791–806. 10.1007/s11053-019-09577-3.
   Web of Science ®Google Scholar
• X. Zhang, H. Nguyen, X.N. Bui, Q.H. Tran, D.A. Nguyen, D.T. Bui, H. Moayedi, Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost, Nat. Resour. Res. 29 (2020) 711–721. 10.1007/s11053-019-09492-7.
   Web of Science ®Google Scholar
• H. Nguyen, C. Drebenstedt, X.N. Bui, D.T. Bui, Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network, Nat. Resour. Res. 29 (2020) 691–709. 10.1007/s11053-019-09470-z.
   Web of Science ®Google Scholar
• Y. Shang, H. Nguyen, X.N. Bui, Q.H. Tran, H. Moayedi, A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network, Nat. Resour. Res. 29 (2020) 723–737. 10.1007/s11053-019-09503-7.
   Web of Science ®Google Scholar
• C.K. Arthur, V.A. Temeng, Y.Y. Ziggah, Multivariate Adaptive Regression Splines (MARS) approach to blast-induced ground vibration prediction, Int. J. Mining, Reclam. Environ. 34 (2020) 198–222. 10.1080/17480930.2019.1577940.
   Web of Science ®Google Scholar
• S. Xu, Y. Li, J. Liu, F. Zhang, Optimization of blasting parameters for an underground mine through prediction of blasting vibration, JVC/Journal Vib. Control 25 (2019) 1585–1595. 10.1177/1077546319829938.
   Google Scholar
• H. Nguyen, X.N. Bui, Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest, Nat. Resour. Res. 28 (2019) 893–907. 10.1007/s11053-018-9424-1.
   Web of Science ®Google Scholar
• K. Ram Chandar, V.R. Sastry, C. Hegde, A critical comparison of regression models and artificial neural networks to predict ground vibrations, Geotech. Geol. Eng. 35 (2017) 573–583. 10.1007/s10706-016-0126-3.
   Google Scholar
• E.T. Mohamad, D. Jahed Armaghani, M. Hasanipanah, B.R. Murlidhar, M.N.A. Alel, Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique, Environ. Earth Sci. 75 (2016) 1–15. 10.1007/s12665-015-4983-5.
   Web of Science ®Google Scholar
• A. Wetherelt, K.P. Van der Wielen, Introduction to open-pit mining, in: P. Darling (Ed.), SME Min. Eng. Handb. 3rd ed., Society for Mining, Metallurgy & Exploration, UK, 2011: pp. 857–876. https://www.smenet.org/publications-resources/publications/mining-engineering-handbook.
   Google Scholar
• S.P. Upadhyay, H. Askari-Nasab, Truck-shovel allocation optimisation: A goal programming approach. Trans. Institutions Min. Metall. Sect. A Min. Technol. 125 (2016) 82–92. 10.1179/1743286315Y.0000000024.
   Google Scholar
• C.N. Burt, L. Caccetta, Match factor for heterogeneous truck and loader fleets, Int. J. Mining, Reclam. Environ. 21 (2007) 262–270. 10.1080/17480930701388606.
   Google Scholar
• O.F. Ugurlu, C. Fan, B. Jiang, W.V. Liu, Deep neural network models for improving truck productivity prediction in open-pit mines, Mining, Metall. Explor. 41 (2024) 619–636. 10.1007/s42461-024-00924-4.
   Google Scholar
• C. Zheng, L. Liu, Y. Meng, M. Wang, X. Jiang, Passable area segmentation for open-pit mine road from vehicle perspective, Eng. Appl. Artif. Intell. 129 (2024) 107610. 10.1016/j.engappai.2023.107610.
   Google Scholar
• C. Fan, N. Zhang, B. Jiang, W.V. Liu, Weighted ensembles of artificial neural networks based on Gaussian mixture modeling for truck productivity prediction at open-pit mines, Mining, Metall. Explor. 40 (2023) 583–598. 10.1007/s42461-023-00747-9.
   Google Scholar
• D. Huo, J. Chen, H. Zhang, Y. Shi, T. Wang, Intelligent prediction for digging load of hydraulic excavators based on RBF neural network, Meas. J. Int. Meas. Confed. 206 (2023) 112210. 10.1016/j.measurement.2022.112210.
   Web of Science ®Google Scholar
• S. Wang, Y. Wu, L. Hou, Z. Yang, Predictive modeling for soft measurement of loader driveshaft torque based on large-scale distributed data, Meas. J. Int. Meas. Confed. 210 (2023) 112566. 10.1016/j.measurement.2023.112566.
   Web of Science ®Google Scholar
• D. Huo, Y.A. Sari, R. Kealey, Q. Zhang, Reinforcement learning-based fleet dispatching for greenhouse gas emission reduction in open-pit mining operations, Resour. Conserv. Recycl. 188 (2023) 106664. 10.1016/j.resconrec.2022.106664.
   Web of Science ®Google Scholar
• S. Alamdari, M. Hossein Basiri, A. Mousavi, A. Soofastaei, Application of machine learning techniques to predict haul truck fuel consumption in open-pit mines, J. Min. Environ. 13 (2022) 69–85. 10.22044/jme.2022.11577.2145
   Web of Science ®Google Scholar
• Y. Choi, H. Nguyen, X.N. Bui, T. Nguyen-Thoi, Optimization of haulage-truck system performance for ore production in open-pit mines using big data and machine learning-based methods, Resour. Policy 75 (2022) 102522. 10.1016/j.resourpol.2021.102522.
   Google Scholar
• L. Wang, X. Xu, Q. Su, Y. Song, H. Wang, M. Xie, Automatic gear shift strategy for manual transmission of mine truck based on Bi-LSTM network, Expert Syst. Appl. 209 (2022) 118197. 10.1016/j.eswa.2022.118197.
   Web of Science ®Google Scholar
• P. Nobahar, Y. Pourrahimian, F. Mollaei Koshki, Optimum fleet selection using machine learning algorithms–case study: Zenouz Kaolin mine, Mining 2 (2022) 528–541. 10.3390/mining2030028.
   Google Scholar
• M. Aliabadi, E. Darvishi, M. Farhadian, R. Rahmani, M. Shafiee Motlagh, N. Mahdavi, An investigation of musculoskeletal discomforts among mining truck drivers with respect to human vibration and awkward body posture using random forest algorithm, Hum. Factors Ergon. Manuf. 32 (2022) 482–493. 10.1002/hfm.20965.
   Web of Science ®Google Scholar
• E. Köken, A.I. Lawal, M. Onifade, A. Özarslan, A comparative study on power calculation methods for conveyor belts in mining industry, Int. J. Mining, Reclam. Environ. 36 (2022) 26–45. 10.1080/17480930.2021.1949859.
   Web of Science ®Google Scholar
• Y. Choi, H. Nguyen, X.N. Bui, T. Nguyen-Thoi, S. Park, Estimating ore production in open-pit mines using various machine learning algorithms based on a truck-haulage system and support of Internet of Things, Nat. Resour. Res. 30 (2021) 1141–1173. 10.1007/s11053-020-09766-5.
   Web of Science ®Google Scholar
• Q. Wang, R. Zhang, S. Lv, Y. Wang, Open-pit mine truck fuel consumption pattern and application based on multi-dimensional features and XGBoost, Sustain. Energy Technol. Assessments 43 (2021) 100977. 10.1016/j.seta.2020.100977.
   Web of Science ®Google Scholar
• X. Sun, X. Li, D. Xiao, Y. Chen, B. Wang, A method of mining truck loading volume detection based on deep learning and image recognition, Sensors (Switzerland) 21 (2021) 635. 10.3390/s21020635
   PubMedGoogle Scholar
• J.P. de Carvalho, R. Dimitrakopoulos, Integrating production planning with truck-dispatching decisions through reinforcement learning while managing uncertainty, Minerals 11 (2021) 587. 10.3390/min11060587.
   Web of Science ®Google Scholar
• Q. Wang, R. Zhang, Y. Wang, S. Lv, Machine learning-based driving style identification of truck drivers in open-pit mines, Electron. 9 (2020) 19. 10.3390/electronics9010019.
   Google Scholar
• D. Xiao, H. Li, C. Liu, Q. He, Large-truck safety warning system based on lightweight SSD model, Comput. Intell. Neurosci. 2019(2019) 10. 10.1155/2019/2180294.
   Web of Science ®Google Scholar
• D. Ali, S. Frimpong, Artificial intelligence models for predicting the performance of hydro-pneumatic suspension struts in large capacity dump trucks, Int. J. Ind. Ergon. 67 (2018) 283–295. 10.1016/j.ergon.2018.06.005.
   Web of Science ®Google Scholar
• X. Sun, H. Zhang, F. Tian, L. Yang, The use of a machine learning method to predict the real-time link travel time of open-pit trucks, Math. Probl. Eng. 2018(2018) 14. 10.1155/2018/4368045.
   Web of Science ®Google Scholar
• B. Ozdemir, M. Kumral, Appraising production targets through agent-based Petri net simulation of material handling systems in open pit mines, Simul. Model. Pract. Theory 87 (2018) 138–154. 10.1016/j.simpat.2018.06.008.
   Web of Science ®Google Scholar
• S.R. Dindarloo, E. Siami-Irdemoosa, Data mining in mining engineering: results of classification and clustering of shovels failures data, Int. J. Mining, Reclam. Environ. 31 (2017) 105–118. 10.1080/17480930.2015.1123599.
   Web of Science ®Google Scholar
• M. Yeganejou, M. Badiozamani, A. Moradi-Afrapoli, H. Askari-Nasab, Integration of simulation and dispatch modelling to predict fleet productivity: an open-pit mining case, Min. Technol. Trans. Inst. Min. Metall. 131 (2021) 67–79. 10.1080/25726668.2021.2001255.
   Web of Science ®Google Scholar
• N. Dendle, E. Isokangas, P. Corry, Efficient simulation for an open-pit mine, Simul. Model. Pract. Theory 117 (2022) 102473. 10.1016/j.simpat.2021.102473.
   Web of Science ®Google Scholar
• S.Q. Liu, Z. Lin, D. Li, X. Li, E. Kozan, M. Masoud, Recent research agendas in mining equipment management: A review, Mining 2 (2022) 769–790. 10.3390/mining2040043
   Google Scholar
• S.Q. Liu, E. Kozan, An interactive planning and scheduling framework for optimising pits-to-crushers operations, Ind. Eng. Manag. Syst. 11 (2012) 94–102. 10.7232/iems.2012.11.1.094.
   Google Scholar
• D. Ali, M.B. Hayat, L. Alagha, O.K. Molatlhegi, An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal, Adv. Powder Technol. 29 (2018) 3493–3506. 10.1016/j.apt.2018.09.032.
   Web of Science ®Google Scholar
• K. Tang, Y. Da Wang, Y. Niu, T.A. Honeyands, D.O. Dea, P. Mostaghimi, R.T. Armstrong, M. Knackstedt, Particle classification of iron ore sinter green bed mixtures by 3D X-ray microcomputed tomography and machine learning, Powder Technol. 415 (2023) 118151. 10.1016/j.powtec.2022.118151.
   Web of Science ®Google Scholar
• L. Windrim, A. Melkumyan, R.J. Murphy, A. Chlingaryan, R. Leung, Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data, Geosci. Front. 14 (2023) 101562. 10.1016/j.gsf.2023.101562.
   Google Scholar
• Y. Xu, B. Klein, G. Li, B. Gopaluni, Evaluation of logistic regression and support vector machine approaches for XRF based particle sorting for a copper ore, Miner. Eng. 192 (2023) 108003. 10.1016/j.mineng.2023.108003.
   Google Scholar
• N.S. Bajaj, A.D. Patange, R. Jegadeeshwaran, S.S. Pardeshi, K.A. Kulkarni, R.S. Ghatpande, Application of metaheuristic optimization based support vector machine for milling cutter health monitoring, Intell. Syst. with Appl. 18 (2023) 200196. 10.1016/j.iswa.2023.200196.
   Google Scholar
• Y.J. Cruz, M. Rivas, R. Quiza, R.E. Haber, F. Castaño, A. Villalonga, A two-step machine learning approach for dynamic model selection: a case study on a micro milling process, Comput. Ind. 143 (2022) 103764. 10.1016/j.compind.2022.103764.
   Web of Science ®Google Scholar
• D.J. Díaz-Romero, S. Van den Eynde, W. Sterkens, A. Eckert, I. Zaplana, T. Goedemé, J. Peeters, Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches, Spectrochim. Acta Part B At. Spectrosc. 196 (2022) 106519. 10.1016/j.sab.2022.106519.
   Web of Science ®Google Scholar
• A. Gomez-Flores, G.W. Heyes, S. Ilyas, H. Kim, Prediction of grade and recovery in flotation from physicochemical and operational aspects using machine learning models, Miner. Eng. 183 (2022) 107627. 10.1016/j.mineng.2022.107627.
   Google Scholar
• B. Zhao, S. Hu, X. Zhao, B. Zhou, J. Li, W. Huang, G. Chen, C. Wu, K. Liu, The application of machine learning models based on particles characteristics during coal slime flotation, Adv. Powder Technol. 33 (2022) 103363. 10.1016/j.apt.2021.11.015.
   Web of Science ®Google Scholar
• H. Dong, F. Wang, D. He, Y. Liu, The intelligent decision-making of copper flotation backbone process based on CK-XGBoost, Knowledge-Based Syst. 243 (2022) 108429. 10.1016/j.knosys.2022.108429.
   Web of Science ®Google Scholar
• M. Ai, Y. Xie, Z. Tang, J. Zhang, W. Gui, Deep learning feature-based setpoint generation and optimal control for flotation processes, Inf. Sci. (Ny). 578 (2021) 644–658. 10.1016/j.ins.2021.07.060.
   Web of Science ®Google Scholar
• Z. Wen, C. Zhou, J. Pan, T. Nie, C. Zhou, Z. Lu, Deep learning-based ash content prediction of coal flotation concentrate using convolutional neural network, Miner. Eng. 174 (2021) 107251. 10.1016/j.mineng.2021.107251.
   Google Scholar
• K. Son, J. Lee, H. Hwang, W. Jeon, H. Yang, I. Sohn, Y. Kim, H. Um, Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks, J. Mater. Res. Technol. 12 (2021) 555–568. 10.1016/j.jmrt.2021.02.085.
   Google Scholar
• Y. Liu, Z. Zhang, X. Liu, L. Wang, X. Xia, Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size, Miner. Eng. 172 (2021) 107020. 10.1016/j.mineng.2021.107020.
   Google Scholar
• Y. Liu, Z. Zhang, X. Liu, L. Wang, X. Xia, Deep learning-based image classification for online multi-coal and multi-class sorting, Comput. Geosci. 157 (2021) 104922. 10.1016/j.cageo.2021.104922.
   Web of Science ®Google Scholar
• L. Tuşa, M. Kern, M. Khodadadzadeh, R. Blannin, R. Gloaguen, J. Gutzmer, Evaluating the performance of hyperspectral short-wave infrared sensors for the pre-sorting of complex ores using machine learning methods, Miner. Eng. 146 (2020) 106150. 10.1016/j.mineng.2019.106150.
   Google Scholar
• Y. Fu, B. Yang, Y. Ma, Q. Sun, J. Yao, W. Fu, W. Yin, Effect of particle size on magnesite flotation based on kinetic studies and machine learning simulation, Powder Technol. 376 (2020) 486–495. 10.1016/j.powtec.2020.08.054.
   Web of Science ®Google Scholar
• B. Xiao, S. Miao, Q. Gao, Quantifying particle size and size distribution of mine tailings through deep learning approach of autoencoders, Powder Technol. 397 (2022) 117088. 10.1016/j.powtec.2021.117088.
   Web of Science ®Google Scholar
• Y. Bi, C. Chen, X. Huang, H. Wang, G. Wei, Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN), Energy 262 (2022) 131932. 10.1016/j.energy.2022.125368.
   Web of Science ®Google Scholar
• P. Li, M. Ali Khan, M.G. Ahmed, H. Hassan Awan, A. Zafar, M. Faisal Javed, M. Ijaz Khan, S. Qayyum, M.Y. Malik, F. Wang, Sustainable use of chemically modified tyre rubber in concrete: machine learning based novel predictive model, Chem. Phys. Lett. 793 (2022) 139478. 10.1016/j.cplett.2022.139478.
   Web of Science ®Google Scholar
• L. Ma, C. Huang, Z.S. Liu, K.A. Morin, M. Aziz, C. Meints, Artificial neural network for prediction of full-scale seepage flow rate at the equity silver mine, Water. Air. Soil Pollut. 231 (2020) 1191–1199. 10.1007/s11270-020-04541-x.
   Web of Science ®Google Scholar
• L. Ma, C. Huang, Z.S. Liu, K.A. Morin, M. Aziz, C. Meints, The correlation between drainage chemistry and weather for full-scale waste rock piles based on artificial neural network, J. Contam. Hydrol. 239 (2021) 103793. 10.1016/j.jconhyd.2021.103793.
   PubMed Web of Science ®Google Scholar
• E. Rodovalho, O. Quaglio, W.S. Felsch Junior, R. Pascual, G. de Tomi, J.A. Soares Tenório, Reducing GHG emissions through efficient tire consumption in open pit mines, J. Clean. Prod. 255 (2020) 120185–120185. 10.1016/j.jclepro.2020.120185.
   Web of Science ®Google Scholar
• Y. Yang, W. Zhou, Z. Wang, I.M. Jiskani, Y. Yang, Accurate long-term dust concentration prediction in open-pit mines: A novel machine learning approach integrating meteorological conditions and mine production intensity, J. Clean. Prod. 436 (2024) 140411. 10.1016/j.jclepro.2023.140411.
   Web of Science ®Google Scholar
• Y. Zhang, L. Wei, Q. Lu, Y. Zhong, Z. Yuan, Z. Wang, Z. Li, Y. Yang, Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images, Environ. Pollut. 320 (2023) 120962. 10.1016/j.envpol.2022.120962.
   PubMed Web of Science ®Google Scholar
• S. Bandyopadhyay, S.K. Maiti, Application of statistical and machine learning approach for prediction of soil quality index formulated to evaluate trajectory of ecosystem recovery in coal mine degraded land, Ecol. Eng. 170 (2021) 106351. 10.1016/j.ecoleng.2021.106351.
   Google Scholar
• X. Lu, W. Zhou, C. Qi, H. Luo, D. Zhang, B.T. Pham, Prediction into the future: a novel intelligent approach for PM2.5 forecasting in the ambient air of open-pit mining, Atmos. Pollut. Res. 12 (2021) 101084. 10.1016/j.apr.2021.101084.
   Google Scholar
• L. Li, R. Zhang, J. Sun, Q. He, L. Kong, X. Liu, Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm, J. Environ. Heal. Sci. Eng. 19 (2021) 401–414. 10.1007/s40201-021-00613-0.
   Web of Science ®Google Scholar
• C. Qi, W. Zhou, X. Lu, H. Luo, B.T. Pham, Z.M. Yaseen, Particulate matter concentration from open-cut coal mines: a hybrid machine learning estimation, Environ. Pollut. 263 (2020) 114517. 10.1016/j.envpol.2020.114517.
   PubMed Web of Science ®Google Scholar
• D. He, B.T. Le, D. Xiao, Y. Mao, F. Shan, T.T.L. Ha, Coal mine area monitoring method by machine learning and multispectral remote sensing images, Infrared Phys. Technol. 103 (2019) 103070. 10.1016/j.infrared.2019.103070.
   Web of Science ®Google Scholar
• Y. Vasuki, L. Yu, E.J. Holden, P. Kovesi, D. Wedge, A.H. Grigg, The spatial-temporal patterns of land cover changes due to mining activities in the Darling Range, Western Australia: a visual analytics approach, Ore Geol. Rev. 108 (2019) 23–32. 10.1016/j.oregeorev.2018.07.001.
   Web of Science ®Google Scholar
• P. Bimpong, B. Addai, S.K. Achinah, Analysis of the causal nexus between mining exports and the environment,Resour. Policy 79 (2022) 103003. 10.1016/j.resourpol.2022.103003.
   Web of Science ®Google Scholar
• R.G. Dimitrakopoulos, S.A. Abdel Sabour, Evaluating mine plans under uncertainty: Can the real options make a difference?, Resour. Policy 32 (2007) 116–125. 10.1016/j.resourpol.2007.06.003.
   Google Scholar
• W. Pan, S.Q. Liu, M. Kumral, A. D’Ariano, M. Masoud, W.A. Khan, A. Bakather, Iron ore price forecast based on a multi-echelon tandem learning model, Nat. Resour. Res. (2024)
   Google Scholar
• S.Q. Liu, X. Huang, X. Li, M. Masoud, S.-H. Chung, Y. Yin, How is China’s energy security affected by exogenous shocks? Evidence of China-US trade dispute and COVID-19 pandemic, Discov. Energy 1 (2021) 2. 10.1007/s43937-021-00002-6.
   Google Scholar
• A. Alipour, A.A. Khodaiari, A. Jafari, R. Tavakkoli-Moghaddam, An integrated approach to open-pit mines production scheduling, Resour. Policy 75 (2022) 102459. 10.1016/j.resourpol.2021.102459.
   Google Scholar
• P. Nancel-Penard, N. Morales, F. Cornillier, A recursive time aggregation-disaggregation heuristic for the multidimensional and multiperiod precedence-constrained knapsack problem: An application to the open-pit mine block sequencing problem, Eur. J. Oper. Res. 303 (2022) 1088–1099. 10.1016/j.ejor.2022.04.005.
   Web of Science ®Google Scholar
• E. Bozorgebrahimi, R.A. Hall, G.H. Blackwell, Sizing equipment for open pit mining - A review of critical parameters, Inst. Min. Metall. Trans. Sect. A Min. Technol. 112 (2003) 171–179. 10.1179/037178403225003591.
   Web of Science ®Google Scholar
• A. Hazrathosseini, A. Moradi Afrapoli, Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems, Min. Technol. 133 (2024) 50–73. 10.1177/25726668231222998.
   Google Scholar
• A. Hazrathosseini, A. Moradi Afrapoli, The advent of digital twins in surface mining: Its time has finally arrived, Resour. Policy 80 (2023) 103155. 10.1016/j.resourpol.2022.103155.
   Google Scholar
• S.Q. Liu, E. Kozan, Integration of mathematical models for ore mining industry, Int. J. Syst. Sci. Oper. Logist. 6 (2019) 55–68. 10.1080/23302674.2017.1344330.
   Web of Science ®Google Scholar
• E. Kozan, S.Q. Liu, An operational-level multi-stage mine production timetabling model for optimally synchronising drilling, blasting and excavating operations, Int. J. Mining, Reclam. Environ. 31 (2017) 457–474. 10.1080/17480930.2016.1160818.
   Web of Science ®Google Scholar
• B. Ozdemir, M. Kumral, Simulation-based optimization of truck-shovel material handling systems in multi-pit surface mines, Simul. Model. Pract. Theory 95 (2019) 36–48. 10.1016/j.simpat.2019.04.006.
   Web of Science ®Google Scholar
• M. Schulze, J. Rieck, C. Seifi, J. Zimmermann, Machine scheduling in underground mining: an application in the potash industry, OR Spectr. 38 (2016) 365–403.
   Web of Science ®Google Scholar
• S.Q. Liu, E. Kozan, P. Corry, M. Masoud, K. Luo, A real-world mine excavators timetabling methodology in open-pit mining, Springer US, 2022. 10.1007/s11081-022-09741-4.
   Google Scholar
• C. Seifi, M. Schulze, J. Zimmermann, A new mathematical formulation for a potash-mine shift scheduling problem with a simultaneous assignment of machines and workers, Eur. J. Oper. Res. 292 (2021) 27–42. 10.1016/j.ejor.2020.10.007.
   Web of Science ®Google Scholar
• M. Ostermeier, A. Heimfarth, A. Hübner, The multi-vehicle truck-and-robot routing problem for last-mile delivery, Eur. J. Oper. Res. 310 (2023) 680–697. 10.1016/j.ejor.2023.03.031.
   Web of Science ®Google Scholar
• M.F. Del Castillo, R. Dimitrakopoulos, Dynamically optimizing the strategic plan of mining complexes under supply uncertainty, Resour. Policy 60 (2019) 83–93.
   Google Scholar
• Q. Zhang, S.Q. Liu, A. D’Ariano, S.H. Chung, M. Masoud, X. Li, A bi-level programming methodology for decentralized mining supply chain network design, Expert Syst. Appl. 250 (2024) 123904. 10.1016/j.eswa.2024.123904.
   Web of Science ®Google Scholar
• M.R.F. da Silva, I.R.S. Agostino, E.M. Frazzon, Integration of machine learning and simulation for dynamic rescheduling in truck appointment systems, Simul. Model. Pract. Theory 125 (2023) 102747. 10.1016/j.simpat.2023.102747.
   Web of Science ®Google Scholar
• J. Renaud, R. Karam, M. Salomon, R. Couturier, Deep learning and gradient boosting for urban environmental noise monitoring in smart cities, Expert Syst. Appl. 218 (2023) 119568. 10.1016/j.eswa.2023.119568.
   Web of Science ®Google Scholar
• Y. Liu, W. Heng, H. Yue, Quantifying the coal mining impact on the ecological environment of Gobi open-pit mines, Sci. Total Environ. 883 (2023) 163723. 10.1016/j.scitotenv.2023.163723.
   PubMed Web of Science ®Google Scholar
• X. He, Y. Wei, J. Holguin-Veras, Brownian bridge-based speed imputation technique for truck energy consumption and emissions estimation, Transp. Res. Part D Transp. Environ. 114 (2023) 103546. 10.1016/j.trd.2022.103546.
   Web of Science ®Google Scholar
• L. Zeng, S.Q. Liu, E. Kozan, R. Burdett, M. Masoud, S. Chung, Designing a resilient and green coal supply chain network under facility, Comput. Ind. Eng. (2023) In press. 10.1016/j.cie.2023.109476.
   Google Scholar
• E. Kozan, S.Q. Liu, A demand-responsive decision support system for coal transportation, Decis. Support Syst. 54 (2012) 665–680. 10.1016/j.dss.2012.08.012.
   Web of Science ®Google Scholar
• S.Q. Liu, E. Kozan, Scheduling trains with priorities: a no-wait blocking parallel-machine job-shop scheduling model, Transp. Sci. 45 (2011) 175–198. 10.1287/trsc.1100.0332.
   Web of Science ®Google Scholar
• Y. He, H. Ma, W. Park, S.Q. Liu, S. Chung, Maximizing robustness of aircraft routing with heterogeneous maintenance tasks, Transp. Res. Part E 177 (2023) 103237. 10.1016/j.tre.2023.103237.
   Google Scholar
• R. Basso, B. Kulcsár, I. Sanchez-Diaz, X. Qu, Dynamic stochastic electric vehicle routing with safe reinforcement learning, Transp. Res. Part E Logist. Transp. Rev. 157 (2022) 102496. 10.1016/j.tre.2021.102496.
   Web of Science ®Google Scholar
• Y. Zhang, R. Bai, R. Qu, C. Tu, J. Jin, A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties, Eur. J. Oper. Res. 300 (2022) 418–427. 10.1016/j.ejor.2021.10.032.
   Web of Science ®Google Scholar
• S. Filom, A.M. Amiri, S. Razavi, Applications of machine learning methods in port operations – A systematic literature review, Transp. Res. Part E Logist. Transp. Rev. 161 (2022) 102722. 10.1016/j.tre.2022.102722.
   Web of Science ®Google Scholar
• P. Corry, C. Bierwirth, The berth allocation problem with channel restrictions, Transp. Sci. 53 (2019) 708–727. 10.1287/trsc.2018.0865.
   Web of Science ®Google Scholar
• J.M. Leal Gomes Leite, E.F. Arruda, L. Bahiense, L.G. Marujo, Modeling the integrated mine-to-client supply chain: a survey, Int. J. Mining, Reclam. Environ. 34 (2020) 247–293. 10.1080/17480930.2019.1579693.
   Web of Science ®Google Scholar