Optimum Fleet Selection Using Machine Learning Algorithms—Case Study: Zenouz Kaolin Mine

This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best selection of the fleet. Th...

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Bibliographic Details
Published in:Mining (Basel) 2022-09, Vol.2 (3), p.528-541
Main Authors: Nobahar, Pouya, Pourrahimian, Yashar, Mollaei Koshki, Fereidoun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Coal mining
Control algorithms
Cost control
Energy consumption
fleet management
Integer programming
Linear programming
Machine learning
Machinery
Methods
mine planning
Mines
mining optimization
Neural networks
Optimization
Productivity
Simulation
Trucks
Unmanned aerial vehicles
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Summary:This paper presents the machine learning (ML) method, a novel approach that could be a profitable idea to optimize fleet management and achieve a sufficient output to reduce operational costs, by diminishing trucks’ queuing time and excavators’ idle time, based on the best selection of the fleet. The performance of this method was studied at the Zenouz kaolin mine to optimize the type of loader and the number of trucks used to supply the processing plant’s ore demands. Accordingly, five years’ data, such as dates, weather conditions, number of trucks, routes, loader types, and daily hauled ore, were collected, adapted, and processed to train the following five practical algorithms: linear regression, decision tree, K-nearest neighbour, random forest, and gradient boosting algorithm. By comparing the results of the algorithms, the gradient boosting decision tree algorithm was determined to be the best fit and predicted test data values with 85% accuracy. Subsequently, 11,322 data were imported into the machine as various scenarios and daily hauled minerals as output results were predicted for each working zone individually. Finally, the data which had the minimum variation from the selected required scheduled value, and its related data concerning loader type and the number of demanded trucks, were indicated for each day of the working year.
ISSN:2673-6489
2673-6489
DOI:10.3390/mining2030028