Micro saint sharp simulation software: micro saint sharp simulation software.
ABSTRACT For the past nineteen years, Micro Saint simulation software has been helping the military and other commercial companies answer questions on how to improve performance and utilization for their various processes. Recently, Micro Saint has been redesigned to be faster, modular and more powerful. Because these changes represent such a major change from the original Micro Saint, we are releasing a brand new tool called Micro Saint Sharp. Micro Saint Sharp is still a general purpose tool that can be used to provide solutions ranging from queuing problems involving hospital waiting rooms to complex human decision processes involving future command and control systems. This paper will provide an overview of Micro Saint Sharp and present some of its new modeling capabilities.
Proceedings of the 2003 Winter Simulation Conference
S. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, eds.
MICRO SAINT SHARP SIMULATION SOFTWARE
Wendy K. Bloechle
Micro Analysis & Design
4949 Pearl East Circle, Ste. 300
Boulder, CO 80301, U.S.A.
For the past nineteen years, Micro Saint simulation soft-
ware has been helping the military and other commercial
companies answer questions on how to improve perform-
ance and utilization for their various processes. Recently,
Micro Saint has been redesigned to be faster, modular and
more powerful. Because these changes represent such a
major change from the original Micro Saint, we are releas-
ing a brand new tool called Micro Saint Sharp. Micro
Saint Sharp is still a general purpose tool that can be used
to provide solutions ranging from queuing problems in-
volving hospital waiting rooms to complex human decision
processes involving future command and control systems.
This paper will provide an overview of Micro Saint Sharp
and present some of its new modeling capabilities.
Discrete event simulation has been a standard technique in
the analysis of manufacturing systems for years. Attention
to the value of simulation in evaluating alternative strate-
gies to reduce costs has also increased in other industries as
a result of competitive pressure and rising costs. Simula-
tion is now being used to evaluate and improve efficiency
in a myriad of areas, including: process definition, quality
measurement and control, process re-design, employee
workload, safety and productivity. With decision-makers
applying simulation technology to a wider variety of prob-
lems, the need for general purpose simulation tools that are
capable of addressing all these needs has increased. Micro
Saint Sharp, a task network based modeling tool, is recog-
nized to be an efficient and cost-effective tool for simulat-
ing the complexities of systems within a variety of indus-
tries ranging from the military to health care. Problems
being analyzed within these industries range from process
control and resource utilization, to military maintenance
procedures and human performance.
The purpose of this paper is to provide a basic under-
standing of the principles of modeling with Micro Saint
Sharp. Micro Saint Sharp does not use the terminology or
graphic representations of a specific industry. If the sys-
tem can be drawn as a flow chart, then you can build a
model of your process in Micro Saint Sharp.
The degree of model complexity is flexible. A simple,
functional model can be built just by drawing a network dia-
gram and filling in the task timing information. Also a more
complex model that includes dynamically changing vari-
ables, probabilistic and tactical branching logic, sorted
queues, conditional task execution, and extensive data col-
lection can be built in Micro Saint Sharp. Micro Saint Sharp
includes a fully functional programming language. This so-
phisticated programming language will make it more effi-
cient to write code especially for large, complex models.
Whether the model is simple or complex, the process of
running the model and generating statistics and graphs from
the collected data is relatively simple. The user simply se-
lects the execution settings and the variable data he/she
wants collected. Micro Saint Sharp symbolically animates
the network diagram as it executes the model, using a ran-
dom number seed the user provides to generate task times
and routing choices specific to the current run. After run-
ning the model, the user can select statistics charts, scatter
plots, line or step bar graphs, bar charts, and frequency dis-
tributions to analyze any data you collected. In addition,
Micro Saint Sharp automatically collects queue data.
One of the key features in Micro Saint Sharp is the cus-
tomizable development environment. While the software is
provided with a default setting when the program is initially
opened, the user can move the palettes and tools around to
create a display that will be efficient for his/her use.
Animation development is provided by a tool called
Animator. A major drawback of some simulation software
packages is that there is only one view of the process that is
available – either the flow chart or the animation. This can
make model debugging time consuming and difficult for the
simulation modeler. Micro Saint Sharp offers two views of
your process. With the network diagram view, you can
animate the process flow chart and change task ovals to any
appearance you choose. With the Animator module, users
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can show a more realistic picture of the process with moving
components. Animator provides the capability for charts
and graphs, text, and images to all be displayed while the
model is running on one screen. In addition, once the ani-
mation is created, users will be able to send the animation
portion of the model to others and to view it through the
Animator player without having to own Micro Saint Sharp.
Micro Saint Sharp was developed specifically with the
goal of increasing model execution speed. While users
will still be able to control the speed of model execution,
they now can run the model with all of the interfaces
turned off. This mode will increase execution speed by a
factor of 10. So when the goal is to have results as quickly
as possible, this mode will allow simulation modelers to
turn off the visual components.
Modularity is also unique to Micro Saint Sharp. Many
simulation tools come with all the components in one pack-
age whether you need them or not. Because of Micro Saint
Sharp’s new plug-in interface and object oriented model de-
velopment, it is easier to do integration. Not only with other
external software applications but with its own individual
tools. The optional tools available with Micro Saint Sharp
include optimization using OptQuest, animation using Ani-
mator, and interoperability using COM Services.
In the following sections, an overview of how to build
a model in Micro Saint is provided.
3 MODEL DEVELOPMENT
The process of building a Micro Saint Sharp model in-
volves two separate but interrelated steps. First, the user
must define the structure of the task network. This is done
by either dragging a task or network object from the palette
onto the task network diagram or by right clicking on the
network diagram and selecting the menu item “Add Task”
(see Figure 2).
2 MODELING ELEMENTS
Figure 2: Task Network Context Menu
Micro Saint Sharp uses a methodology known as task net-
work modeling. Activities are represented in a diagram as
nodes and the arrows between the nodes represent the se-
quence in which the activities are performed. A simple
task network is shown in Figure 1.
Saint Sharp uses the windows standard “point and click”
approach to define network objects. Using the mouse to
“double-click” on an object will open it so that information
specific to the object may be entered. Micro Saint Sharp
also has a “Properties” window allowing the user to edit
any object’s properties after it has been selected. The fol-
lowing section explains the task parameters in more detail.
Second is to define the objects in the network. Micro
Figure 1: Simple Task Network
same techniques they would use to define a flow diagram of
the activity. Each activity, whether it is a human activity or
a system activity, is defined using the same method.
A Micro Saint Sharp model is composed of “net-
works” which may be a sequence of tasks to be performed
by a human, a series of processes that define an organiza-
tion, or a machine in a manufacturing plant. Networks are
composed of either lower-level networks or “tasks.” Tasks
represent the lowest level in the model and have specific
parameters (timing information, conditions for execution,
beginning and ending effects).
3.1 Task Timing Information
This approach allows users to develop models using the
The task “mean time” is the average time that a task takes
to complete once it has begun executing. For example, if
the task represents a human activity such as “transfer pa-
tient to recovery room,” then the mean time to execute is
the average time that it takes to perform the task. If the
task represents a machine in a manufacturing process, then
the mean time to execute is the average processing time for
the machine. In many cases the execution time is not con-
stant, rather, the elapsed time falls within a range of values
that can be represented by a time distribution. Micro Saint
supports more than 21 distribution types including normal,
rectangular, exponential, gamma, Weibull, Poisson and
others. In addition, users may enter their own parameters
to control the spread of the distribution.
Bloechle and Schunk
current state of the system or by an attribute of the process
itself. In human performance modeling, the mean time to
perform a task may be influenced by such conditions as how
long the human has been working, the skill level of the hu-
man, or the current workload. In an insurance claim proc-
essing model, the type of claim or the location of the client
may determine the time it takes to process a claim.
In Micro Saint Sharp any information that is used by
the engine to make calculations must be “returned” to the
engine. This is done in the mean time and standard devia-
tion description boxes with the code “return” followed by
either a number or variable representing the mean time or
standard deviation (see Figure 3). Micro Saint Sharp will
use this number in its calculation of the task duration time.
Alternatively, the mean time may be determined by the
Figure 3: Mean Time and Std Dev Return Code
3.2 Conditions for Execution
Often, there are situations where a task cannot begin exe-
cuting until certain conditions are met. A customer cannot
make a transaction at a bank, even though the queue is
empty, until a bank teller is available. A task may have re-
source requirements or other constraints (i.e., time of day,
part type) that dictate when the task may begin executing.
In Micro Saint Sharp, a Boolean (logical) expression is
evaluated in the “release condition” field to control the
execution of tasks. The release condition description box
is another box which needs the “return” code. This return
expression is then used to evaluate whether a task can be
started or not. Entities moving through the network, such
as patients, parts, or claim forms, cannot be released into a
task for processing until the release conditions for the task
have been met.
3.3 Beginning/Ending Effects
The current state of the system may change when a task be-
gins or ends. For example, when a machine begins process-
ing a part it becomes “busy” and is not available to another
part until it has finished. The user would define the follow-
ing expressions in Micro Saint to define this condition:
Release Condition: if busy == 0 then return 1 else
return 0; This keeps an entity (e.g. part, patient,
etc.) from moving into the task when the task is
“busy” processing another entity.
Beginning Effect: busy = 1; This sets the busy
flag to TRUE so that the next entity cannot enter
the task. As long as the task is executing, the
busy flag will remain equal to “1”.
Ending Effect: busy = 0; When the task finishes
executing, the ending effect is evaluated and the
busy flag is set to 0. Now, when the release con-
dition is evaluated, the condition will be true and
the next entity can enter the task.
This relationship between the release condition and the
beginning and ending effects provides a general, yet pow-
erful mechanism for users to define complex behaviors
within the system they are modeling. Users may define
variables that are specific to their system and manipulate
the value of the variable as needed so that they can accu-
rately represent their system. They do not have to com-
promise the accuracy of their model by relying on pre-
defined “blocks” within the modeling tool nor do they have
to learn a complex programming language in order to ob-
tain the level of control required.
3.4 Task Sequencing
Task sequencing is defined by clicking on the diamond
after a task and dragging with the mouse from the first task
to the following task(s). Users then enter the conditions
that control the branching; this includes tasks with just one
exiting path or multiple exiting paths. Micro Saint pro-
vides the following decision types to ensure that all real-
world situations may be represented in the model:
Probabilistic: The following task conditions are
evaluated and the next task to execute is deter-
mined by the relative probabilities of all tasks
listed. Only one of the following tasks will be
executed with probabilistic decisions.
Multiple: The following task conditions are
evaluated and all of the tasks whose conditions
evaluate to non-zero will execute.
Tactical: The following task conditions are evalu-
ated and the next task to execute is the task whose
condition evaluates to the highest value.
Variables and algebraic expressions can be used in the
branching logic and the value of the variables can be
changed by conditions in the model. This gives the user
complete control and manipulation of the network flow.
All of these features provide an environment for the
model developer that is easy to learn and easy to use.
Once the basic concepts are understood, any system or
process can be modeled using Micro Saint. In addition,
users can build models at any level of complexity.
Bloechle and Schunk
Variables allow a user to change any aspect of a simulation
model. A user defines variables based upon what informa-
tion he/she is trying to get out of a model. Variables are
defined by the user by editing variable properties. These
properties can be accessed by clicking on a variable in the
variable list and editing that variable via the properties
window (see Figure 4). Micro Saint Sharp supports the
following variable types.
Integer: Any number that can be counted in whole
numbers (e.g. people, parts)
Floating Point: Any number that could have a
decimal point (e.g. time, temperature)
String: Words and phrases
Boolean: true and false logic statements
Entity: Points to a specific entity inside the simu-
Variables can also be defined inside the model locally
by declaring the variable type and name. Micro Saint Sharp
also supports variables specific to entities called entity at-
tributes. This allows the user to specify specific attributes of
every entity inside a Micro Saint Sharp model. These spe-
cific entity attributes could then be used for data collection
or to affect how an entity could flow through the model.
Micro Saint Sharp has two types of functions: built-in
functions, which are accessible from all models, and cus-
tom functions, which the user defines within a particular
model. When Micro Saint Sharp encounters the function,
it executes the function and returns a value that can be used
in the expression. Custom functions are particularly useful
for calculations or procedures that the user wants to exe-
cute at more than one place in a model. For example, a
function to calculate machine utilization might be created
and used to calculate utilization for various machines.
6 ANALYSIS AND RESULTS
People build models to provide insight to, or to answer
specific questions about, a system or process. Some in-
formation can be gained by watching the Micro Saint
Sharp model run. Micro Saint Sharp’s symbolic animation
capability provides an animated view of the network dia-
gram as the model is running. Users can watch as entities
flow through the network or wait in queues before being
processed. This type of animation is particularly useful in
debugging the model.
Sometimes it is sufficient to save the state of the sys-
tem at the end of the run. However, in order to gain insight
into the dynamic aspects of the system, users can “take
snapshots” of the model variables at any time during the
run. These “snapshots” of data can be analyzed by import-
ing them into another statistical analysis package. In addi-
tion, data can be collected at any time during the model
run. Micro Saint Sharp automatically collects data for
every queue in a model. In addition, task, trace and re-
source data files can be collected automatically.
Micro Saint Sharp includes a real time graphing capa-
bility. This graphing capability allows the user to watch data
be dynamically graphed onto any type of chart. These charts
can then be printed or saved as separate files for later analy-
sis (see Figure 4). Using the insights gained from the results
of the simulation analysis, users can assess the relative mer-
its of alternative solutions. Additionally, users can predict
the impact of these solutions which subsequently leads to a
better understanding of the costs and benefits.
Figure 4: Network Animation and Charts
Micro Saint Sharp also has an image-based animation
capability called Animator. This allows custom animations
of the model to be built. The background scene can be a
three-dimensional diagram from a CAD package, a digi-
tized diagram of a factory floor, or a bitmap from any
drawing package. The creation and movement of the ani-
mation is driven by events that take place in the model.
Animator allows descriptions of the model to be shown
while the model is executing and is an extremely valuable
tool for presenting the model. It can also be run in real
time. Using Animator, the user can have annotations, la-
bels, plots, graphs and images all on the same screen. Im-
ages can be scaled and rotated with ease. After a model
has been executed, the animation run can be saved and
played later via the Animator player.
7 BANK TELLER MODEL
The bank teller model represents a simple, classic queuing
simulation model. In this model, the bank opens at 9am
and people are no longer admitted into the bank after 5pm.
During lunchtime, a larger number of people come into the
Bloechle and Schunk
bank to perform their transactions. There are currently two
bank tellers on staff during the day.
7.1 Define Variables
The variables used in this model can be broken out into
two different categories: model variables, variables that de-
fine how the model itself will run and data collection vari-
ables, variables used specifically for data collection.
The model variables are:
Rate: defines the current rate of arrival of bank
Tellers: defines the current number of available
tellers at any given moment during a model run
The data collection variables are:
Waittotal: used to calculate the total wait time of
every entity in the model
Avwait: used to calculate the average wait time of
the entities in the model
Maxwait: used to calculate the maximum time an
entity had to wait in the model
Servetotal: used to calculate the total service time
of every entity in the model
Utilization: used to calculated the utilization by
Maxtellers: defines the maximum number of tell-
ers available, used to calculate the utilization by
Waited: used to calculate wait time by customer
Served: used to calculate serve time by customer
Figure 6: Entity Tag Code
Figure 7: Customers will Stop Arriving into the Bank after
After a customer has arrived at the bank, he/she needs
to get in line to be served. If there is no line, then the cus-
tomer is immediately served. When the customer starts to
be served, data is collected so that teller utilization can be
calculated (see Figure 8).
7.2 Define Task Network
The task network consists of two tasks that every entity
will perform: arrive at the bank and be served at the bank.
The entity in this model is the bank customer. The task
network is shown in Figure 5.
Figure 5: Bank Teller Task Network
Entity arrivals are defined by the Rate variable and will ac-
count for the possibility of entities entering the system at
different rates. Entities are also tagged after they arrive
into the system in order to make each one a unique entity
in the system (see Figure 6).
When an entity finishes its arrival task, the next arrival
needs to be scheduled unless the bank is not permitting any
more people to arrive. This decision type is described by
multiple paths: one for the next arrival and one for the cus-
tomer to get in line to be served. When the simulation
clock is past the standard eight hour work day, no custom-
ers will be allowed to enter the bank (see Figure 7).
Figure 8: Utilization Calculation Code
wait in line. The line will be a first in, first out (FIFO)
queue. Data collection is useful here in order to analyze
customer satisfaction. Average customer wait time and
maximum customer wait time will be calculated.
If all the tellers are busy, then the customer needs to
7.3 Define Scenario Events
The scenario of the model will need to define all the initial-
izing factors of the model as well as account for the rate of
arrival change of the customers at noontime. A series of