394
Next section presents a brief overview of the literature followed by introducing the integrated simulation and optimization framework for simulating mining operation, and the
mixed integer linear programming model developed to solve the truck dispatching problem.
Afterwards, the application of the integrated framework and the mixed integer linear programming model in a real case study is presented.
2 RELATED LITERATURE
Simulation is the imitation of the operation of a real-world process or system over time
(Banks 2001). The power of simulation as a tool to evaluate operating systems has been
accepted worldwide. In the literature of the simulation, it origins in a simple mathematical
problem called the Buffon’s needle dated to 1777. The application of simulation in the mining sector can be traced back to 1940s. However, the credit of the first use of discrete event
simulation was given to Rist (1961) who used the Monte Carlo simulation technique to solve
the hauling problem in mining operations.
Developments in the capabilities of computers in the 1980s helped researchers conduct
studies on the models and observe deficiencies, through the use of computer programs and
simulations (Lynch & Morrison 1999). After the first usage of the simulation in the mining
operation, several studies have been done by different researches in the field. To evaluate various dispatching techniques and prove positive impacts of implementing dispatching techniques in mining operations Sturgul & Eharisson (1987), Bonates & Lizotte (1988), Forsman
et al. (1993), Kolonja & Mutmansky (1994), and Ataeepour & Baafi (1999) implemented
simulation modeling.
Awuah-Offei et al. (2003) implemented simulation modeling for determination of fleet size
in case of both truck and shovels a mine. To mimic the dynamic expansion of an open pit
mine, Askari-Nasab et al. (2007) developed a simulator called open pit production simulator
(OPPS). Their study shows that in the cases of modeling dynamicity of the processes and
randomness of the input parameters, artificial intelligent simulators can be very efficient and
helpful. Fioroni et al. (2008) used discrete event simulation and linked it with an optimization
model to deal with the short-term production plan. The goal of the study was to show how
simulation and optimization are integrated to achieve a reasonable solution to this problem. To analyze and evaluate the effects of equipment breakdown on the utilization of the
resources and production of the operation, Yuriy & Vayenas (2008) combined discrete event
simulation with a genetic algorithm based reliability assessment model.
From 2010 to 2015 all simulation studies in the field of truck and shovel mining system
including Jaoua et al. (2012), Jaoua et al. (2012), Mena et al. (2013), Ta et al. (2013), Hashemi
and Sattarvand (2015), Torkamani & Askari-Nasab (2015), Upadhyay & Askari-Nasab
(2017), and Upadhyay & Askari-Nasab (2016) are using the simulation as a tool to evaluate
results of the developed optimization algorithm in their studies without incorporating a new
component into their system.
Dindarloo et al. (2015) provide a step by step discrete event simulation guideline for truckshovel mining system equipment selection. The claim in the study is that the framework helps
to minimize errors caused by inaccurate assumptions as well as procedures.
In one of the latest simulation study of a truck-shovel mining system, Que et al. (2016)
investigated how ignoring the correlation between the input parameters will impact on the
results of the study. The research presents a new approach to detect and import correlated
parameters into the truck-shovel simulation study. Instead of the independent distributions,
the new approach generates a multivariate random vector representing input parameters into
the simulation modeling.
Beside all efforts mentioned above, some review studies related to the implementation of
the simulation in the mining sector have been done since the late 1990s. Sturgul (1999) provides a historical review of discrete mine system simulation in the United States. Vagenas
(1999) present a review of the application of simulations in Canada, and Konyukh et al.
(1999) did a review study over the implementation of the simulation in Asia. Raj et al. (2009)
Next section presents a brief overview of the literature followed by introducing the integrated simulation and optimization framework for simulating mining operation, and the
mixed integer linear programming model developed to solve the truck dispatching problem.
Afterwards, the application of the integrated framework and the mixed integer linear programming model in a real case study is presented.
2 RELATED LITERATURE
Simulation is the imitation of the operation of a real-world process or system over time
(Banks 2001). The power of simulation as a tool to evaluate operating systems has been
accepted worldwide. In the literature of the simulation, it origins in a simple mathematical
problem called the Buffon’s needle dated to 1777. The application of simulation in the mining sector can be traced back to 1940s. However, the credit of the first use of discrete event
simulation was given to Rist (1961) who used the Monte Carlo simulation technique to solve
the hauling problem in mining operations.
Developments in the capabilities of computers in the 1980s helped researchers conduct
studies on the models and observe deficiencies, through the use of computer programs and
simulations (Lynch & Morrison 1999). After the first usage of the simulation in the mining
operation, several studies have been done by different researches in the field. To evaluate various dispatching techniques and prove positive impacts of implementing dispatching techniques in mining operations Sturgul & Eharisson (1987), Bonates & Lizotte (1988), Forsman
et al. (1993), Kolonja & Mutmansky (1994), and Ataeepour & Baafi (1999) implemented
simulation modeling.
Awuah-Offei et al. (2003) implemented simulation modeling for determination of fleet size
in case of both truck and shovels a mine. To mimic the dynamic expansion of an open pit
mine, Askari-Nasab et al. (2007) developed a simulator called open pit production simulator
(OPPS). Their study shows that in the cases of modeling dynamicity of the processes and
randomness of the input parameters, artificial intelligent simulators can be very efficient and
helpful. Fioroni et al. (2008) used discrete event simulation and linked it with an optimization
model to deal with the short-term production plan. The goal of the study was to show how
simulation and optimization are integrated to achieve a reasonable solution to this problem. To analyze and evaluate the effects of equipment breakdown on the utilization of the
resources and production of the operation, Yuriy & Vayenas (2008) combined discrete event
simulation with a genetic algorithm based reliability assessment model.
From 2010 to 2015 all simulation studies in the field of truck and shovel mining system
including Jaoua et al. (2012), Jaoua et al. (2012), Mena et al. (2013), Ta et al. (2013), Hashemi
and Sattarvand (2015), Torkamani & Askari-Nasab (2015), Upadhyay & Askari-Nasab
(2017), and Upadhyay & Askari-Nasab (2016) are using the simulation as a tool to evaluate
results of the developed optimization algorithm in their studies without incorporating a new
component into their system.
Dindarloo et al. (2015) provide a step by step discrete event simulation guideline for truckshovel mining system equipment selection. The claim in the study is that the framework helps
to minimize errors caused by inaccurate assumptions as well as procedures.
In one of the latest simulation study of a truck-shovel mining system, Que et al. (2016)
investigated how ignoring the correlation between the input parameters will impact on the
results of the study. The research presents a new approach to detect and import correlated
parameters into the truck-shovel simulation study. Instead of the independent distributions,
the new approach generates a multivariate random vector representing input parameters into
the simulation modeling.
Beside all efforts mentioned above, some review studies related to the implementation of
the simulation in the mining sector have been done since the late 1990s. Sturgul (1999) provides a historical review of discrete mine system simulation in the United States. Vagenas
(1999) present a review of the application of simulations in Canada, and Konyukh et al.
(1999) did a review study over the implementation of the simulation in Asia. Raj et al. (2009)