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

Abstract

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.

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