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Simulation - Concepts and Applications.



Simulation in last decades has been widely used to analyze the impact of different scenarios in several areas like, for instance, health, military, business, and many others. When well used, it is an excellent tool to analyze alternative actions and to anticipate their impact, in order to rationalize the spending of resources. This paper introduces and resumes some of the main concepts of simulation, identifying and describing: systems; models; entities and attributes; resources; contexts of use; and, in particularly, the discrete-event simulation.
M.D. Lytras et al. (Eds.): WSKS 2010, Part II, CCIS 112, pp. 429–434, 2010.
© Springer-Verlag Berlin Heidelberg 2010
Simulation – Concepts and Applications
Pedro Sá Silva1, António Trigo1, João Varajão2,3, and Tiago Pinto2
1 Coimbra Institute of Accounting and Administration, Portugal
2 University of Trás-os-Montes e Alto Douro, Portugal
3 Centro ALGORITMI, Portugal,,,
Abstract. Simulation in last decades has been widely used to analyze the impact
of different scenarios in several areas like, for instance, health, military, business,
and many others. When well used, it is an excellent tool to analyze alternative
actions and to anticipate their impact, in order to rationalize the spending of
resources. This paper introduces and resumes some of the main concepts of
simulation, identifying and describing: systems; models; entities and attributes;
resources; contexts of use; and, in particularly, the discrete-event simulation.
Keywords: simulation, system, model, entity, resource, discrete-event simulation.
1 Introduction
“Simulation application’s areas are only limited by the imagination of the user.” [1].
In fact, simulation is used in many different contexts. It practically does not have
limits and can be applied to any system that fits the concepts of simulation modeling
[2]. Manufacturing systems (ex. optimization of production lines and logistics), public
systems (ex. emergency vehicle dispatch and weather forecast), military systems (ex.
warfare scenarios and training), transportations systems (ex. railroad and air transpor-
tation), constructing systems (ex. test buildings), computer systems (ex. computer
networks and computer games), are some examples of applications [1-5]. In the fol-
lowing sections it will be introduced several important concepts of simulation, and
discussed aspects like why and when use simulation for problem solving. Finally, it
will be presented some final remarks on the current state of the art of simulation.
2 Simulation
There is not one universal definition for simulation. Nevertheless, all definitions fol-
low the same general concept: simulation is an imitation of a system [4-8]. System
imitation involves the construction of an artificial history, with the real system fea-
tures [4, 7]. This allows a better understanding of the system including how it works,
behaves and evolves over time.
430 P. Sá Silva et al.
2.1 Why Simulate?
“Simulation is an indispensable problem-solving methodology for the solution of
many real-world problems” [7]. Daily we benefit from the result of many simulations,
like, for instance, simulation of a loan or the weather forecast. Probably the most
famous and successful simulation product in history is the video game. Who has
never played video game? In video games, scenarios are created from real systems or
imaginary systems, with many entities (people, cars, monsters, etc.), where the play-
ers play the same game many times but always with different results (the player may
lose or win, win with a better or a lower score, etc.). On larger scale issues, like build-
ing a production line for a new car or building sky scrapers, the magnitude of the
problem increases, but the simulation’s concepts still remain. In these contexts, simu-
lation’s techniques allow a cost-effect study before commitment of resources (in last
example, before building). A good simulation can increase the performance of a proc-
ess, rationalize resources, time and costs [1, 2, 4, 9]. The simulation becomes even
more important when human lives may be in danger situations, as for instance in
firemen training.
2.2 When Simulate?
The use of simulation as a tool to understand the dynamic of a system it is not the
only approach available. Simulation analysts may experiment the system by changing
the real system itself [3] in spite of simulation. But studying the real system with this
approach is not always possible, usually is more expensive and more disruptive [3].
Imagine, for instance, optimizing the number of bank tellers in a bank branch. To
analyze this case without simulation, the analyst probably would need to test different
numbers of bank tellers in a certain period of time to analyze performance in serving
the clients. Imagine the cost that this study approach would have for the bank (ex. cost
of the bank tellers, when there are more than necessary, and the bank image to the
client, when there are less bank tellers than the necessary). This type of approach may
work in small and simple systems where changes do not affect the integrity and the
normal function of the system. Other approach could be the system study through
analytical methods. In last example, the analyst could summarize the problem through
a simple mathematical expression. But if the study goals were to understand the ten-
dencies for the next two years, probably it would be very difficult to summarize this
through a simple mathematical expression. In this case the simulation would be the
best approach to understand the system behavior. Simulation is a proper tool to ana-
lyze systems, when they are so complex that are impossible to solve through analyti-
cal methods [8].
3 General Concepts
For some authors simulation is both an art and a science [7, 9]. The art part, like other
arts, only can be developed through time and experience. Like any science there are
rules and concepts that must be followed. For a better understanding of the simulation
as a science, this section exposes the main concepts associated: system; model; enti-
ties and attributes; and resources.
Simulation – Concepts and Applications 431
3.1 System
A system is a collection of interrelated entities working together to achieve some goal
[2-4]. The system that is object of study may aggregate many other sub-systems, and
one of these sub-system may aggregate many others sub-systems [10, 11]. In order to
avoid analyzing an endless system it is necessary to limit the system scope, dependent
on the study goals [3, 4, 7]. Systems are influenced by their environment since they
receive inputs that cause systems changes and produce results (outputs). These
changes are represented by state variables, which together define the system state. In
other words, system state variables contains the necessary data to describe the system
state in a particular moment of simulation [3, 7]. A common system variable used in
simulation is the CurrentTime [6] (except in simulations where time is irrelevant).
3.2 Model
To create a system model, first the analyst needs to understand the system behavior
and indentify its boundaries. The model is a simplified representation of the system
[2]. It represents the dynamics and behaviors of the system, allowing a better under-
stand of it and helping to predict the impact of these changes [2, 7, 9, 10]. Ideally the
changes that occur in a real system have to be represented in the model [10] to enable
answers to the “what if” questions [7]. The system may be modulated through a
physical or a mathematical model. In a physical model, the imitation of the system is
made using physical resources [3], as for instance, car games that have a small car
replica with the basic components (steering wheel, brake pedal, accelerator pedal,
etc.). Such models usually have less interest to researchers [3]. In mathematical mod-
els, the system behavior is expressed in logical and quantitative relationships. There
are two types of mathematical models [3]: analytical solutions or simulation. The
analytical solutions are used when the system’s relationships are easy to quantify [8].
If the system is so complex that it is impossible to represent it through an analytical
solution, then simulation must be used.
Simulation models can be classified into tree different dimensions [3, 4]: static or
dynamic; deterministic or stochastic; continuous or discrete. The first dimension has to
do with time [1, 3]. For instance, imagine that someone wants to find how to improve
motor power. In such study, the system is not influenced by time. However if the ob-
ject of study was to increase engine reliability, time must be considered for the simula-
tion. This case is dynamic simulation. The second dimension identifies if the system
has probabilistic characteristics or not [1]. In other words, when the system behaves in
an unpredicted form is used the stochastic model [2, 3]. For instance, the study of the
market acceptance of an engine is a stochastic simulation model because the market
depends on many random variables (client needs, market state, fashion, etc.). The third
dimension has to do with how often the system state changes. In continuous simulation
models, the system state changes continually over time, whereas in a discrete simula-
tion model the system state only changes in some particular moments in time [3, 4].
3.3 Entities and Attributes
Entities are the main objects or components of a system that require an explicit repre-
sentation on the model [4, 7] (ex. clients, computers, etc.). Entities and its relationships
432 P. Sá Silva et al.
define the system behavior, i.e., entities cause changes in the system state. Without
them nothing would happen in simulation [6]. Each entity has its own attributes, which
are identified characteristics (or qualities) that are very important to understand the
entity role in the system [2, 6] that helps predicting the system behavior. Different
entities may have similar attributes, but, according to the study goals, one attribute may
be interesting for a special study and uninteresting for another [7]. In a football game
for instance, the entities would be the football players, the coach and the referee. The
attributes would be running speed, physical condition, moral, etc. Every entity has run
speed, but only the player speed is important.
3.4 Resources
A resource is an object with limited capacity [6] that exist to serve entities and can be
many things, like machines, computers, workers or cars. An entity may even be a
resource, this happens when an entity serves another entity. Banks [7] defines this
type of resources as static entity and the other ones as dynamic entities. For instance,
in a supermarket case, when a client asks information to supermarket seller about a
product, the dynamic entity is the client and the static entity is the seller, because the
seller serves the client. Resources are not always available to serve, i.e. they have
states and limits. A resource may have many different states, like “idle”, “busy” or
“blocked” [7]. The state is very important because it is needed to define if the re-
source is able to serve or not. Normally one resource serves one entity at time, how-
ever some resources may serve many entities at same time (according to their limits)
operating as a parallel server [7]. The limited capacity of resources increases the
probability of resource competition between entities, i.e., two or more entities at-
tempting to use the same resource at same time.
4 Discrete-Event Simulation
DES is widely used on simulation world [2], because it allows to study complex sys-
tems in a simple way. In most of the systems the state of the system change at sepa-
rate moments in time [2-5, 10] and, according to this approach, only what happens in
these moments is relevant. In DES, the interval of time between two events, is irrele-
vant, because, according to this model, nothing happens in these intervals [10]. The
most famous application areas are the queuing and scheduling examples. For instance,
imagine the simulation of a store. When there are people to buy products, the system
state is “busy”, but when there is anybody in the store, the system state is “idle”. This
state does not have interest for analyses, because nothing happens in that period. In
this type of simulation, time has an important role [7, 9]. For a better understanding,
following will be presented the main concepts of DES: events and activities; random-
ness and statistics.
4.1 Events, Activities and Processes
An event is an occurrence that may modify the system state [3, 6, 7, 9]. Taking the pre-
vious store example, the arrival of a client is an event, because the system state changes
from “idle” to “busy”. We may have internal (endogenous) or external (exogenous)
Simulation – Concepts and Applications 433
events [7]. An external event starts out of the system and an internal event starts inside
the system. An activity is a pre-known length of a time between two events, which
generally involves some action [2, 4, 7], normally represents a service time [4]. “An
entity interacts with activities. Entities interacting with activities create events” [6]. The
activity duration time may be deterministic, statistical or dependent [4]. In a determinis-
tic activity, the duration time is previously known, in the statistical activity the duration
time is unpredictable and the duration of time of a dependent activity is conditioned to
other variables. The last two types of activities may origin an unknown time activity, in
other words, a delay. A delay is an indefinite length of time, caused by some system
conditions [6]. A process is a sequence of events or activities that are logically con-
nected [2, 9]. To illustrate all of this concepts imagine a supermarket example. The
process “buying” start with the arrival of the customer (external event) and ends with
the exits from the store (internal event). The client buying activity (adds products to
shopping cart) is a statistical activity and when he finish shopping is an internal event. If
he waits in pay line this is a delay. If he pays with credit card, the time needs to finish
the transaction it is a depended activity.
4.2 Randomness and Statistics
There are systems that react in a predictable form and others that react in an unpre-
dictable form. Systems that react in a predictable form are easy to modulate, simulate
and understand, but systems that react in an unpredictable form are more complex to
understand because they are subjective and uncertain. In others words, they have a
random or stochastic behavior. A good example is a football game simulation. Know-
ing that team A is superior to B can you state that A will always win? The game has
so many uncertain variables, like the player physical conditions, player motivation,
weather conditions, type of field, etc., that combined may change the game result. A
model with an uncertain behavior (stochastic) uses random numbers to mimic the
uncertainty of the system. To get randomness into simulation model the analyst will
need a random numbers generator (RNG). In practice the RNG only generate random
numbers uniformly distributed between zero and one. There are many techniques to
generate random numbers, like the two dice technique and the Pseudo-Random
Number Generator [10]. Once our system has randomness, the only way to analyze
simulation results is by using statistical methods. Therefore there must exist a tool
responsible for collecting data needed for statistical analyses, usually known as statis-
tical collector [6].
5 Conclusion
Simulation is used on many different contexts. This demonstrates the flexibility of
this tool to help solve problems and somewhat explains is success. Simulation power
comes from the possibility of analyzing and understanding a complex system, offer-
ing the possibility of predicting by showing how the different entities are structured
and related, how the system evolves and changes.
As final remark, it important to note that simulation is not always the best approach
to solve problems, there are limitations and drawbacks [3-5, 7, 9]. Before simulating
434 P. Sá Silva et al.
anything, it is necessary to evaluate if simulation advantages overcome simulation
disadvantages. Given the huge list of areas where simulation is used, it is undeniable
that simulation has and will continue to have an important role on human lives.
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