Mining Goes Digital – Mueller et al. (Eds)
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
393
Simulation and optimization framework for evaluating
the mining operations
A. Moradi Afrapoli & H. Askari-Nasab
Mining Optimization Laboratory (MOL), School of Mining Engineering, Department of Civil
and Environmental Engineering, University of Alberta, Canada
ABSTRACT: This paper presents a MILP model to solve truck dispatching problem in
surface mines. As testing a new technology in mining operation is expensive, simulation
is used as a tool to evaluate effects of new technologies in the surface mining operations.
However, two significant drawbacks exist in currently available simulation models. The available models ignore the existence of decision-making tools such as mining fleet management
systems in mining operations. Also, the available simulation models disregard impacts of
processing plants on the mining operation. To test our MILP model, this paper presents an
intelligent simulation and optimization framework to mimic the surface mining operations.
The given framework covers the drawbacks mentioned above. We implemented the framework in a surface mine case study to compare performance of our MILP model with its in
place truck dispatching model. Results of the comparison show more than 10% improvement
in production of the case study.
1 INTRODUCTION
Surface mining is an expensive operation. Most of the currently active surface mines implement different decision making tools for making closer to optimal decisions in their daily
operations to cut a portion of the expensive operational cost. Several decision making models
are developed every day to help in the cost reduction of a surface mine. Testing a new tool
and evaluating its performance in a real mining operation is costly. Therefore, simulation
modeling has been widely applied as one of the cheapest ways to test a decision making
model in surface mining operations since the 1960s. However, the thus far developed simulation models have ignored the critical role of the decision making tools as a component of the
surface mining operation system. However, in most of the active mines across the globe, each
step of mining is tied closely to the optimal decisions being made by an optimization tool.
In this paper, we present an integrated simulation and optimization framework using
which; a surface mining operation can be simulated with as many decision making tools as
the mine is using in its daily operation. The framework consists of three major components:
the simulation tool, the optimization tool, and the mine’s dataset. The components of the
framework are linked to each other and information, decisions, and data are transferred
between the components during the simulation.
The developed integrated simulation and optimization framework was implemented to
simulate a surface mining operation that uses two mathematical models for making optimal
decisions in its production optimization and truck dispatching problems. The paper investigates the answer to “what if we replace the current truck dispatching model with a new truck
dispatching model?”. Based on the drawbacks in currently available truck dispatching decision
making models, we developed an Integer linear programming model to solve the truck dispatching problem in surface mines. We substituted currently in place truck dispatching model
in the case study with the developed model in the simulation model. Then we compared the
performance of the developed model against the in place truck dispatching model.
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
393
Simulation and optimization framework for evaluating
the mining operations
A. Moradi Afrapoli & H. Askari-Nasab
Mining Optimization Laboratory (MOL), School of Mining Engineering, Department of Civil
and Environmental Engineering, University of Alberta, Canada
ABSTRACT: This paper presents a MILP model to solve truck dispatching problem in
surface mines. As testing a new technology in mining operation is expensive, simulation
is used as a tool to evaluate effects of new technologies in the surface mining operations.
However, two significant drawbacks exist in currently available simulation models. The available models ignore the existence of decision-making tools such as mining fleet management
systems in mining operations. Also, the available simulation models disregard impacts of
processing plants on the mining operation. To test our MILP model, this paper presents an
intelligent simulation and optimization framework to mimic the surface mining operations.
The given framework covers the drawbacks mentioned above. We implemented the framework in a surface mine case study to compare performance of our MILP model with its in
place truck dispatching model. Results of the comparison show more than 10% improvement
in production of the case study.
1 INTRODUCTION
Surface mining is an expensive operation. Most of the currently active surface mines implement different decision making tools for making closer to optimal decisions in their daily
operations to cut a portion of the expensive operational cost. Several decision making models
are developed every day to help in the cost reduction of a surface mine. Testing a new tool
and evaluating its performance in a real mining operation is costly. Therefore, simulation
modeling has been widely applied as one of the cheapest ways to test a decision making
model in surface mining operations since the 1960s. However, the thus far developed simulation models have ignored the critical role of the decision making tools as a component of the
surface mining operation system. However, in most of the active mines across the globe, each
step of mining is tied closely to the optimal decisions being made by an optimization tool.
In this paper, we present an integrated simulation and optimization framework using
which; a surface mining operation can be simulated with as many decision making tools as
the mine is using in its daily operation. The framework consists of three major components:
the simulation tool, the optimization tool, and the mine’s dataset. The components of the
framework are linked to each other and information, decisions, and data are transferred
between the components during the simulation.
The developed integrated simulation and optimization framework was implemented to
simulate a surface mining operation that uses two mathematical models for making optimal
decisions in its production optimization and truck dispatching problems. The paper investigates the answer to “what if we replace the current truck dispatching model with a new truck
dispatching model?”. Based on the drawbacks in currently available truck dispatching decision
making models, we developed an Integer linear programming model to solve the truck dispatching problem in surface mines. We substituted currently in place truck dispatching model
in the case study with the developed model in the simulation model. Then we compared the
performance of the developed model against the in place truck dispatching model.