Merits of discrete event simulation in modeling mining operations

Abstract

Discrete event simulation is a stochastic mathematical modeling tool with applications in queuing systems. Many mining operations, both surface and underground, can be simulated in the context of queuing theory , for example open pit loading and haulage operation, underground level and vertical transportation, and mineral processing circuits. A computer simulation model of the operation is an invaluable tool to study both the system’s dynamics and conducting sensitivity analysis on different effective elements on system performance. In this paper, merits of discrete event simulation in decision making in mining engineering is discussed. Moreover, a stochastic simulation framework for selection and sizing of shovels and dump trucks in surface mining is presented to elaborate one example for applicability of the method in mining operations.

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1 Copyright © 2016 by SME
Preprint 16-035
MERITS OF DISCRETE EVENT SIMULATION IN MODELING MINING OPERATIONS
S. R. Dindarloo, Missouri Univ. of Science and Tech., Rolla, MO
E. Siami-Irdemoosa, Missouri Univ. of Science and Tech., Rolla, MO
ABSTRACT
Discrete event simulation is a stochastic mathematical modeling
tool with applications in queuing systems. Many mining operations,
both surface and underground, can be simulated in the context of
queuing theory , for example open pit loading and haulage operation,
underground level and vertical transportation, and mineral processing
circuits. A computer simulation model of the operation is an invaluable
tool to study both the system’s dynamics and conducting sensitivity
analysis on different effective elements on system performance. In this
paper, merits of discrete event simulation in decision making in mining
engineering is discussed. Moreover, a stochastic simulation framework
for selection and sizing of shovels and dump trucks in surface mining is
presented to elaborate one example for applicability of the method in
mining operations.
Keywords: Discrete event simulation; queuing theory; decision
making; mining operations; equipment selection and sizing.
INTRODUCTION
Mathematical modeling is a valuable tool in design, optimization,
and decision making in both surface and underground mining
operations. Several researchers have tried to study different aspects of
mining operations using a wide range of mathematical techniques such
as linear programming[1], analytical hierarchy process [2], non-linear
programming[3], genetic algorithms[4-5] ,mixed integer programming
[6], machine repair model[7], queuing theory [8], and conventional
spreadsheet calculations based on experience and engineering
judgments. There are different uncertainties in typical mining
operations (e.g. material loading and haulage, drilling, and blasting).
For example, in a truck-shovel operation, different governing activities
such as loading, haulage, and dumping cycles are stochastic variables.
Moreover, analytical modeling of a large truck-shovel operation is very
complicated. Thus, application of analytical and/or deterministic
techniques cannot guarantee a solution for different problems that are
encountered in daily mining operations. Discrete-event system
simulation (DES) is a modeling method for such time discrete and
probabilistic phenomena [9]. DES has been employed by different
researchers in mining engineering through available software and
languages such as GPSS, SIMAN- ARENA, and SLAM [10-14]. Most
of the studies so far, tried to evaluate some “what-if” scenarios, to
understand the possible effects of changing different input variables on
overall mine economics. For instance, Baffi and Ataeepur (1996) used
Arena to simulate a truck-shovel operation [10]. Also, Sturgul has
conducted extensive studies in the application of DES in the mining
engineering field [15- 17]. A very good background review of
application of this technique in the industry can be found in [15-16].
Previous studies have shown that, different approaches including
deterministic, stochastic, and experimental methodologies result in
considerable differences in outputs [18]. These techniques lead to
different solutions, regardless of the quality of technique/software itself
or knowledge of the modeling team. Hence, the first step is to develop
a comprehensive simulation framework to obtain nearly the same
optimal results for the same input variables, regardless of the
employed technique.
In this paper, a methodology for mine loading and haulage system
selection and sizing is introduced. This framework is based on DES for
solving truck-shovel selection, sizing, and dispatching. In Sec. 2 both
the advantages and disadvantages of DES are introduced. The
simulation framework is presented in Sec. 3. Concluding remarks are
summarized in Sec. 4.
MERITS OF DES
A summary of the most important benefits of DES is as below
[19]:
i) Simulation allows engineers to test every aspect of a
proposed change or addition without committing resources
to their acquisition. This is critical, because once the hard
decisions have been made, the bricks have been laid, or the
material handling systems have been installed, changes and
corrections can be extremely expensive. Simulation allows
testing designs without committing resources to acquisition.
ii) By compressing or expanding time; simulation allows
speeding up or slowing down phenomena so that they can
be thoroughly investigated. One can examine an entire shift
in a matter of minutes.
iii) Managers often want to know why certain phenomena occur
in a real system. With simulation, one determines the answer
to the "why" questions by reconstructing the scene and
taking a microscopic examination of the system to determine
why the phenomenon occurs.
iv) One of the greatest advantages of using simulation software
is that once a valid simulation model has been developed,
new policies, operating procedures, or methods can be
explored without the expense and disruption of
experimenting with the real system. Modifications can be
incorporated in the model, and the effects of those changes
can be observed on the computer rather than the real
system.
v) Simulation models can provide excellent training when
designed for that purpose. Used in this manner, the team
provides decision inputs to the simulation model as it
progresses. The team, and individual members of the team,
can learn by their mistakes, and learn to operate better. This
is much less expensive and less disruptive.
However, like any other mathematical tool, DES has its own
disadvantages as:
i) Model building requires special training. It is an art that is
learned over time and through experience. Furthermore, if
two models of the same system are constructed by two
competent individuals, they may have similarities, but it is
highly unlikely that they will be the same.
ii) Simulation results may be difficult to interpret. Since, most
simulation outputs are essentially random variables (they are
usually based on random inputs), it may be hard to
determine whether an observation is a result of system
interrelationships or randomness.
iii) Simulation modeling and analysis can be time consuming
and expensive. Skimping on resources for modeling and
analysis may result in a simulation model and/or analysis
that are not sufficient for the task.
iv) Simulation may be used inappropriately. Simulation is used
in some cases when an analytical solution is possible, or
even preferable. This is particularly true in the simulation of

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some waiting lines where closed form queueing models are
available.
SIMULATION FRAMEWORK FOR TRUCK-SHOVEL SYSTEM
Lack of a comprehensive simulation framework in this field has
resulted in considerably different solutions to the problem of truck-
shovel system selection and sizing. A major source of these confusing
differences are as below:
• application of different simulation approaches
• different data requirements (quantity, quality, and statistical
methodology)
• insufficient technical communication during all phases of the
project
• insufficient determination of objectives, resources, and
constraints.
In this section, a simulation framework is proposed to minimize
errors due to wrong or inaccurate assumptions/procedures and to
provide a step by step simulation guideline. The following algorithm
tries to render a framework for truck-shovel operation simulation
excercises . In construction of the simulation framework, different
blocks of the following diagram (Figures 1- 2), were obtained from
almost all published journal articles (to the best of authors’ knowledge).
Only the most considerable findings of the previous studies were
incorporated and interconnected in a rational base to achieve an
efficient simulation strategy. The first step for developing this
framework with the goals of completeness, comprehensiveness, and
robustness was to identify the very major components of a general
simulation modeling practice, regardless of the area of its application.
A general simulation framework is illustrated in Fig. 1 .This primary
platform was set to serve as the structure of the framework and
consequently was customized through introducing surface mining
specifications. These specified characteristics were derived from
published articles in the field of mining operations simulation and
modeling and were incorporated to the base structure.
Figure 1. A general simulation flowchart [20].
The base framework was composed of the following components:
1- Problem definition, objectives, resources, and limitations.
2- Data acquisition and statistical processing.
3- Model construction.
4- Model modification, verification, and validation.
5- Sensitivity analysis and decision-making strategies.
However, there are pitfalls in a general simulation practice [21] as
below:
- Unclear objective.
- Invalid model.
- Simulation model too complex or too simple.
- Erroneous assumptions.
- Undocumented assumptions.
- Using the wrong input probability distribution.
- Replacing a distribution (stochastic) by its mean
(deterministic).
- Using the wrong performance measure.
- Bugs in the simulation program.
- Using standard statistical formulas that assume
independence in simulation output analysis.
- Initial bias in output data.
- Making one simulation run for a configuration.
- Poor schedule and budget planning.
- Poor communication among the personnel involved in the
simulation study.
The above pitfalls were incorporated in the proposed simulation
framework for the truck- shovel selection and sizing problem.
Secondary mine-specific characteristics included:
1- Incorporation of the mining environment induced constraints.
2- Different traffic dispatching scenarios.
3- Different loading methods.
4- Selection of hybrid or uniform haulage fleets.
The main advantage of this simulation framework (Fig. 2) is that it
is comprehensive in addressing the problem of truck-shovel selection.
All other available practices (published articles) try to find solutions to
specific parts of the problem, mainly in the form of “what-if” analysis.
For instance, what would be the effect of adding one extra truck to the
haulage fleet? Moreover, the framework is capable of addressing both
a new and existing surface mine operation. However, application of the
proposed DES framework for different projects needs proper
customizations. For instance, production planning strategies in a mine
with restricted processing plant requirements of ore grade limits;
dictate more frequent relocations of working faces, compared with a
mine with more stable and predictable ore grade fluctuations. These
types of differences introduce frequent changes in haulage distances
and, hence, in simulation approach at hand. Another example is the
difference between a small surface mine with more short term
concentrated production plans and a large mine with more strategic
and long term plans. These types of specifications require more/less
consideration of some blocks of the framework than others,
accordingly (Fig. 2).
CONCLUSIONS
Discrete event simulation is a viable alternative to analytical
mathematical techniques. In cases that a closed form solution for a
mining problem is either not available or too difficult to derive;
application of DES is the only alternative. Although, DES has many
advantages, there are some drawbacks in designing, modeling, and
interpreting the results. These pitfalls should be taken into account
before deciding on application of DES. In this paper, a specific DES
simulation framework was offered for a mining problem. Applicability of
the framework needs further numerical validation. There are many
areas in mining operations that can be modeled by DES that need
further research and investigations.

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Figure 2. The proposed simulation framework.
REFERENCES
[1] Edwards D J, Malekzadeh H, Yisa SB.2001. A linear
programming decision tool for selecting the optimum excavator.
Structural Survey; 19(2): 113-120.
[2] Ayağ ZZ. 2007.A hybrid approach to machine-tool selection
through AHP and simulation. International Journal of Production
Research ; 45(9):2029-2050.
[3] Søgaard HT, Sørensen CG. 2004. A Model for Optimal Selection
of Machinery Sizes within the Farm Machinery System.
Biosystems Engineering; 89(1):13-28.
[4] Aghajani A, Osanloo M, Akbarpour M. 2007.Optimising the
loading system of Gol-e-Gohar iron ore mine of iran by genetic
algorithm. Australasian Institute of Mining and Metallurgy
Publication Series; PP 211-215.
[5] Marzouk M, Moselhi O. 2003.Constraint-based genetic algorithm
for earthmoving fleet selection. Canadian Journal of Civil
Engineering 2003; 30(4): 673-683.
[6] Camarena EA, Gracia C, Cabrera Sixto JM. 2004. A Mixed
Integer Linear Programming Machinery Selection Model for
Multifarm Systems. Biosystems Engineering 2004; 87(2):145-154.
[7] Krause A, Musingwini C. 2007. Modelling open pit shovel-truck
systems using the Machine Repair Model. The Journal of the
Southern African Institute of Mining and Metallurgy 2007; 107:
469-476.
[8] Komljenovic D, Paraszczak J, Fytas K. 2004.Optimization of
shovel-truck systems using the queuing theory. CIM Bulletin
2004;97:76.
[9] Schriber TJ.1992. Perspectives on simulation using GPSS.
Proceedings of the 24th conference on Winter simulation;1992.
pp 338-342.
[10] Baffi EY, Ataeepur M. 1996. Simulation of a Truck- Shovel
System using Arena. Proceeding of 26th International Symposium
on the Application of Computers and Operations Research in the
Mineral Industries(APPCOM), Pennsylvania, USA; 1996. pp 153 –
159.
[11] Runciman N, Vagenas N, Newson G. 1996. Simulation Modeling
of Underground Hard - rock Mining Operations Using WITNESS.
Proceedings of the 26th International Symposium on the
Application of Computers and Operation Research in the Mineral
Industries (APCOM), Pennsylvania, USA; 1996. pp 148 – 151.
[12] Awuah-Offei K, Temeng V.A, Al-Hassan S. 2003. Predicting
Equipment Requirements Using SIMAN, A Case Study. Mining
Technology 2003; 112: A180-A184.
[13] Ross I, Casten T, Marsh D, Peppin C. 2010. The role of
simulation in ground handling optimization at the grasberg block
cave mine. Hoist and Haul 2010 - Proceedings of the International
Conference on Hoisting and Haulage 2010: 257-265.
[14] Sturgul JR, Thurgood SR. 1993.Simulation Model for Materials
Handling System for Surface Coal Mine. Balk Solids Handling;
13(4): 817– 820.
[15] Sturgul JR. 1999. Discrete Mine Systems Simulation in the United
States. International Journal of Surface Mining Reclamation and
Environment 1999; 13: 37–41.
[16] Sturgul JR. 1995. Simulation and Animation Come of Age in
Mining. Engineering and Mining Journal 1995: 38– 42.
[17] Hollocks, B.W. 2006. Forty years of discrete-event simulation-a
personal reflection. Journal of the Operational Research Society,
57 (12), pp. 1383-1399.
[18] Burt C, Caccetta L, Hill S, Welgama P. 2005. Models for Mining
Equipment Selection. MODSIM05 - International Congress on
Modelling and Simulation: Advances and Applications for
Management and Decision Making 2005: 1730-1736.
[19] Banks, Jerry.1999.Introduction to simulation. Winter Simulation
Conference Proceedings, 1, pp. 7-13.
[20] Banks, C. 2000. Introduction to Modeling and Simulation,
Modeling and Simulation Fundamentals: Theoretical
Underpinnings and Practical Domains, pp. 1-24.
[21] Maria, A 1997. Introduction to modeling and simulation (1997)
Winter Simulation Conference Proceedings, pp. 7-13.