Optimización de tiempos para reducir costos en carguío y acarreo mediante un modelo de Red Neuronal Artificial

La investigación tuvo como objetivo reducir los costos mediante el control de tiempos y la mejora del mantenimiento de la ruta de acarreo. Se empleó un enfoque cuantitativo y un diseño cuasi experimental, realizando una evaluación de los procesos existentes de carguío y transporte, considerando la distancia recorrida, los equipos utilizados y los tiempos involucrados. Durante 20 guardias se monitorearon las operaciones y se implementaron controles de tiempos de carguío y acarreo, así como la programación de una red neuronal para predecir los costos asociados. Los resultados obtenidos muestran que, al verificar los tiempos y las predicciones con los promedios, existe una diferencia de $0.20 por tonelada métrica. Asimismo, al revisar los tiempos de transporte y el adecuado mantenimiento de la ruta de acarreo, los costos de soporte se redujeron de $1.04 por tonelada métrica a $0.90, logrando un ahorro adicional de $0.14. Además, se concluyó que el control del tiempo y el mantenimiento adecuado de la vía permiten reducir los tiempos de carguío en un 11.45% y los tiempos de transporte en un 12.73% dentro de la unidad minera. Se propone un modelo predictivo para próximas investigaciones basado en redes neuronales que permite optimizar los tiempos de carguío y acarreo, logrando una reducción de hasta 0.34 USD/TM en los costos operativos. Referencias [1] Anticona, J.Y.; Noriega, E.M.; Cotrina, M.A.; Arango, M.S.M. (2024). Evaluation of Predictive Models for the Optimization of the Cost of Unit Operations in Artisanal Underground Mining. Mathematical Modelling of Engineering Problems, 11(4), 2901–2911.DOI: https://doi.org/10.18280/mmep.111103 [2] Baek, J.; Choi, Y. (2017). A New Method for Haul Road Design in Open-Pit Mines to Support Efficient Truck Haulage Operations. Applied Sciences, 7(7), 747.DOI: https://doi.org/10.3390/app7070747 [3] Baek, J.; Choi, Y. (2020). Deep neural network for predicting ore production by truck-haulage systems in open-pit mines. Applied Sciences, 10(5), 1657.DOI: https://doi.org/10.3390/app10051657 [4] Baunier de Melo, W. (2021). Optimization of truck allocation in mines using differential evolution algorithm. International Journal for Innovation Education and Research, 9(8), 338–350.DOI: https://doi.org/10.31686/ijier.vol9.iss8.3303 [5] Cotrina, MA; Araujo, J.J.; Mamani, J.N. et al. (2025). Estimación de recursos minerales mediante cópulas espaciales y aprendizaje automático optimizado con metaheurísticas en un yacimiento de cobre. Earth Sci Inform, 18, 514.DOI: https://doi.org/10.1007/s12145-025-02009-2 [6] Cotrina, MA; Marquina, JJ; Riquelme, Á.I. (2025). Comparación de técnicas de aprendizaje automático para la categorización de recursos minerales en un yacimiento de cobre en Perú. Nat Resour Res, 34, 2007–2025.DOI: https://doi.org/10.1007/s11053-025-10505-x [7] Cotrina, M.A.; Marquina, J.J. (2025). Hybrid Machine Learning Models for Mineral Resource Classification in a Copper Deposit in Peru. Math Geosci.DOI: https://doi.org/10.1007/s11004-025-10247-9 [8] Cotrina, M.; Marquina, J.; Polo, J. (2025). Prediction of unit haulage cost in an underground mine using machine learning techniques. Journal of Sustainable Mining, 24(2), Article 9.DOI: https://doi.org/10.46873/2300-3960.1454 [9] Cotrina, MA; Marquina, JJ; Mamani, J.N. (2025). Application of artificial neural networks for the categorization of mineral resources in a copper deposit in Peru. World Journal of Engineering, ahead-of-print.DOI: https://doi.org/10.1108/WJE-01-2025-0004 [10] Cotrina, M.A.; Vera, J.K.; Arango, S.M. (2025). An Intelligent Approach to Predicting Dilution, Overbreak and Costs in Underground Mining Using Kolmogorov-Arnold Networks. Mathematical Modelling of Engineering Problems, 12(3), 815–828.DOI: https://doi.org/10.18280/mmep.120308 [11] Cotrina, M.; Marquina, J.; Mamani, J.; Arango, S.; Gonzalez, J.; Noriega, E.; Antonio, E. (2025). Hybrid machine learning techniques to predict fuel consumption of dump trucks in an open-pit mine in Peru. International Journal of Mining and Mineral Engineering, 1–20.DOI: https://doi.org/10.1504/IJMME.2025.145583 [12] Enkhchuluun, B.; Batgerel, B.-O.; Ping, C. (2023). Cycle Time Analysis Mining Dump Trucks. International Journal of Geosciences, 14, 689–709.DOI: https://doi.org/10.4236/ijg.2023.148037 [13] Gu, Q.; Lu, C.; Li, F.; Wan, C. (2008). Monitoring Dispatch Information System of Trucks and Shovels in an Open Pit Based on GIS/GPS/GPRS. Journal of China University of Mining and Technology, 18(2), 288–292.DOI: https://doi.org/10.1016/S1006-1266(08)60061-9 [14] Huayanca, D.; et al. (2023). Application of Discrete-Event Simulation for Truck Fleet Management in Mining. Applied Sciences, 13(7).DOI: https://doi.org/10.3390/app13074093 [15] Icarte, G.; Riveros Araya, E.; Herzog, O. (2020). An Agent-based System for Truck Dispatching in Open-pit Mines. ICAART 2020.DOI: https://doi.org/10.5220/0008961800730081 [16] Karikari, Y.S.; Askari Nasab, H. (2024). A Comprehensive Simulation Model for Mining Operations: Development, Implementation, and Validation Using HaulSim. Technical Report, University of Alberta.DOI: https://doi.org/10.13140/RG.2.2.33986.08649 [17] Kecojevic, V.; Komljenovic, D. (2005). Haul truck cycle-time prediction in open-pit mines using artificial neural networks. International Journal of Surface Mining, Reclamation and Environment, 19(1), 1–17.DOI: https://doi.org/10.1080/13895260500032564 [18] Krause, A.; Musingwini, C. (2019). Estimating truck-shovel fleet productivity using discrete-event simulation. International Journal of Mining, Reclamation and Environment, 33(5), 299–313.DOI: https://doi.org/10.1080/17480930.2018.1438522 [19] Mandal, S.K.; Dey, S.; Bhar, C. (2018). Analysis of Factors Which Influence the Cycle Time of Dumpers of Open Cast Coal Mines to Improve Production. Modelling, Measurement and Control C, 78(3), 289–302.DOI: https://doi.org/10.18280/mmc_c.780303 [20] Meneses, D.; Sepúlveda, F.D. (2023). Modeling Productivity Reduction and Fuel Consumption in Open-Pit Mining Trucks Considering Road Deterioration. Mining, 3(1), 96–105.DOI: https://doi.org/10.3390/mining3010006 [21] Mnzool, M.; Almujibah, H.; Bakri, M.; Gaafar, A.; Elhassan, A.A.M.; Gomaa, E. (2024). Optimization of cycle time for loading and hauling trucks in open-pit mining. Mining of Mineral Deposits, 18(1), 18–26.DOI: https://doi.org/10.33271/mining18.01.018 [22] Pathan, S.M.; et al. (2025). Simulation Optimization of Shovel-Truck System in Open-Pit Mines. Applied Technology & Research, 2025.DOI: https://doi.org/10.1155/atr/7939037 [23] Shah, K.S.; Rehman, S.U. (2020). Modeling and Optimization of Truck–Shovel Allocation to Mining Faces in Cement Quarry. Journal of Mining and Environment, 11(1), 21–30.DOI: https://doi.org/10.22044/jme.2019.8329.1712 [24] Shakenov, A.; Sładkowski, A.; Stolpovskikh, I. (2022). Haul Road Condition Impact on Tire Life of Mining Dump Truck. Mining Journal (NVNGU), 2022(6), Art. 025.DOI: https://doi.org/10.33271/nvngu/2022-6/025 [25] Simon, V.; Pellerin, R.; Gamache, M. (2025). Predicting Haul Truck Travel Times in Underground Mines. Mining, Metallurgy & Exploration, 42(4), 1989–2009.DOI: https://doi.org/10.1007/s42461-025-01293-2 [26] Soofastaei, A.; Aminossadati, S.; Kizil, M.S.; Knights, P. (2015). The Effect of Rolling Resistance on Fuel Consumption and Greenhouse Gas Emissions by Haul Trucks in Surface Mines. Tribology International, 92, 222–234.DOI: https://doi.org/10.1016/j.triboint.2015.06.007 [27] Wang, X.; Dai, Q.; Bian, Y.; Zhang, M. (2023). Real-time truck dispatching in open-pit mines. International Journal of Mining, Reclamation and Environment, 37(6), 400–420.DOI: https://doi.org/10.1080/17480930.2023.2201120 [28] Wang, X.; Dai, Q.; Bian, Y.; Xie, G. (2023). Real-time truck dispatching in open-pit mines. International Journal of Mining, Reclamation and Environment, 37(7), 1–20.DOI: https://doi.org/10.1080/17480930.2023.2201120 [29] Zhang, Y.; et Eal. (2022). Determination of Truck–Shovel Configuration of Open-Pit Mines using Simulation and Mathematical Modelling. Sustainability, 14(19), 12338.DOI: https://doi.org/10.3390/su141912338

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.