|تعداد مشاهده مقاله||106,182,343|
|تعداد دریافت فایل اصل مقاله||83,086,583|
Evaluating the efficiency of the genetic algorithm in designing the ultimate pit limit of open-pit mines
|International Journal of Mining and Geo-Engineering|
|مقاله 7، دوره 57، شماره 1، خرداد 2023، صفحه 55-58 اصل مقاله (304.22 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/ijmge.2022.340973.594963|
|Nooshin Azadi1؛ Hossein Mirzaei-Nasirabad* 1؛ Amin Mousavi2|
|1Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran|
|2Faculty of Mining Engineering, Tarbiat Modares University, Tehran, Iran|
|The large-scale open-pit mine production planning problem is an NP-hard issue. That is, it cannot be solved in a reasonable computational time. To solve this problem, various methods, including metaheuristic methods, have been proposed to reduce the computation time. One of these methods is the genetic algorithm (GA) which can provide near-optimal solutions to the problem in a shorter time. This paper aims to evaluate the efficiency of the GA technique based on the pit values and computational times compared with other methods of designing the ultimate pit limit (UPL). In other words, in addition to GA evaluation in UPL design, other proposed methods for UPL design are also compared. Determining the UPL of an open-pit mine is the first step in production planning. UPL solver selects blocks whose total economic value is maximum while meeting the slope constraints. In this regard, various methods have been proposed, which can be classified into three general categories: Operational Research (OR), heuristic, and metaheuristic. The GA, categorized as a metaheuristic method, Linear Programming (LP) model as an OR method, and Floating Cone (FC) algorithm as a heuristic method, have been employed to determine the UPL of open-pit mines. Since the LP method provides the exact answer, consider the basics. Then the results of GA were validated based on the results of LP and compared with the results of FC. This paper used the Marvin mine block model with characteristics of 53271 blocks and eight levels as a case study. Comparing the UPL value's three ways revealed that the LP model received the highest value by comparing the value obtained from GA and the FC algorithm's lowest value. However, the GA provided the results in a shorter time than LP, which is more critical in large-scale production planning problems. By performing the sensitivity analysis in the GA on the two parameters, crossover and mutation probability, the GA's UPL value was modified to 20940. Its UPL value is only 8% less than LP's UPL value.|
|Floating cone algorithm؛ Genetic algorithm؛ Linear programming model؛ Sensitivity analysis؛ Ultimate pit limit|
 Mwangi, A., et al., Ultimate Pit Limit Optimization Methods in Open Pit Mines: A Review. Journal of Mining Science, 2020. 56(4): p. 588-602.
 Osanloo, M., J. Gholamnejad, and B. Karimi, Long-term open pit mine production planning: a review of models and algorithms. International Journal of Mining, Reclamation, and Environment, 2008. 22(1): p. 3-35.
 Newman, A.M., et al., A review of operations research in mine planning. Interfaces, 2010. 40(3): p. 222-245.
 Akbari, A., M. OSANLOU, and M. Shirazi, Determination of ultimate pit limits in open mines using real options approach. International Journal of Engineering Science, 2008. 19: p. 23-38.
 Johnson, T.B., Optimum open pit mine production scheduling. 1968, CALIFORNIA UNIV BERKELEY OPERATIONS RESEARCH CENTER.
 Robert Underwood and B. Tolwinski, A mathematical programming viewpoint for solving the ultimate pit problem. European Journal of Operational Research, 1998. 107(1): p. 11.
 Sattarvand, J. and C. Niemann-Delius, Past, Present and Future of Metaheuristic Optimization Methods in Long-Term Production Planning of Open Pits. BHM Berg- und Hüttenmännische Monatshefte, 2013. 158(4): p. 146-154.
 Franco-Sepúlveda, G., J.C. Del Rio-Cuervo, and M.A. Pachón-Hernández, State of the art about metaheuristics and artificial neural networks applied to open pit mining. Resources Policy, 2019. 60: p. 125-133.
 Katoch, S., S.S. Chauhan, and V. Kumar, A review on the genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021. 80(5): p. 8091-8126.
 Frimpong, S. and P.K. Achireko, The MCS/MFNN algorithm for open pit optimization. International Journal of Surface Mining, Reclamation, and Environment, 2007. 11(1): p. 45-52.
 Shishvan, M.S. and J. Sattarvand, Long-term production planning of open pit mines by ant colony optimization. European Journal of Operational Research, 2015. 240(3): p. 825-836.
 Kumral, M. and P.A. Dowd, A simulated annealing approach to mine production scheduling. Journal of the Operational Research Society, 2005. 56(8): p. 922-930.
 Khan, A. and C. Niemann-Delius, Production scheduling of open pit mines using particle swarm optimization algorithm. Advances in Operations Research, 2014. 2014.
 Alipour, A., et al., An integrated approach to open-pit mines production scheduling. Resources Policy, 2022. 75: p. 102459.
 Espinoza, D., et al., MineLib: a library of open pit mining problems. Annals of Operations Research, 2013. 206(1): p. 93-114.
 Hochbaum, D.S. and A. Chen, Performance analysis and best implementations of old and new algorithms for the open-pit mining problem. Operations Research, 2000. 48(6): p. 894-914.
 Mousavi Nogholi, A.A., Optimisation of open pit mine block sequencing, in Science and Engineering Faculty. 2015, Queensland University of Technology.
 Kumar, M., et al., Genetic algorithm: Review and application. Available at SSRN 3529843, 2010.
 Hassanat, A., et al., Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach. Information, 2019. 10(12): p. 390.
 Pencheva, T., K. Atanassov, and A. Shannon, Modelling of a roulette wheel selection operator in genetic algorithms using generalized nets. International Journal Bioautomation, 2009. 13(4): p. 257.
 Maulik, U. and S. Bandyopadhyay, Genetic algorithm-based clustering technique. Pattern recognition, 2000. 33(9): p. 1455-1465.
 Villalba Matamoros, M.E. and M. Kumral, Calibration of Genetic Algorithm Parameters for Mining-Related Optimization Problems. Natural Resources Research, 2018. 28(2): p. 443-456.
 Ataei, M. and M. Osanloo Using a combination of genetic algorithm and the grid search method to determine optimum cutoff grades of multiple metal deposits. International Journal of Surface Mining, Reclamation, and Environment, 2004. 18(1): p. 60-78.
 Saltelli, A. and P. Annoni, How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software, 2010. 25(12): p. 1508-1517.
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