Authors :
Satyam Choudhury; Dr. H. K. Naik
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Pi36g2
DOI :
https://doi.org/10.5281/zenodo.6812563
Abstract :
In surface mining operations, the dumper
haulage system contributes the most in total operating cost
of any mine. It is estimated that an average mining
company spends around 50% to 60% in this truck haulage
system only. So utmost priority should be given to keep up
an effective haulage framework. So, to reduce the cost of
operation the dumpers must be allocated and dispatched
efficiently. The haulage systems should be designed in such
a manner that the availability, performance and utilization
of the dumper and shovel are maximized, which ultimately
yield in high production and reduction of operating cost.
So, in this paper to enhance the productivity of truck
haulage system an attempt is made to minimize the cycle
time of dumpers and allocate an optimized number of
dumpers to one shovel so that the idle time of dumpers can
be minimized. In determining the cycle, time of dumpers
predicting the travelling time in different situation is given
utmost importance. For the machine learning models are
used which help in predicting the travelling time in
different atmospheric situation of the mine. This approach
of integrating the machine learning methods in minimizing
the cycle time will provide a proper estimation of
performance measure, truck scheduling and finally an
optimized truck dispatch system.
Keywords :
Opencast Mine, Truck Dispatch System, Dumpers, Shovels, Cycle Time, Scheduling, Overall Equipment Effectiveness, Machine Learning, Optimization.
In surface mining operations, the dumper
haulage system contributes the most in total operating cost
of any mine. It is estimated that an average mining
company spends around 50% to 60% in this truck haulage
system only. So utmost priority should be given to keep up
an effective haulage framework. So, to reduce the cost of
operation the dumpers must be allocated and dispatched
efficiently. The haulage systems should be designed in such
a manner that the availability, performance and utilization
of the dumper and shovel are maximized, which ultimately
yield in high production and reduction of operating cost.
So, in this paper to enhance the productivity of truck
haulage system an attempt is made to minimize the cycle
time of dumpers and allocate an optimized number of
dumpers to one shovel so that the idle time of dumpers can
be minimized. In determining the cycle, time of dumpers
predicting the travelling time in different situation is given
utmost importance. For the machine learning models are
used which help in predicting the travelling time in
different atmospheric situation of the mine. This approach
of integrating the machine learning methods in minimizing
the cycle time will provide a proper estimation of
performance measure, truck scheduling and finally an
optimized truck dispatch system.
Keywords :
Opencast Mine, Truck Dispatch System, Dumpers, Shovels, Cycle Time, Scheduling, Overall Equipment Effectiveness, Machine Learning, Optimization.
