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User's guide chapter 3 section 11 & 12



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3.11 – Action Logic

ProcessModel allows you to design custom behavior in your model by using action logic in which you enter simple but powerful logic statements. Action logic allows you to define special logic that may not be easily defined using the normal property fields. Examples would include assigning values to attributes and variables or performing a test using an IF…THEN statement.

Action logic can be defined for any activity, storage, arrival or routing by clicking on the Action tab of the properties dialog.

Depending on the object or connection for which the action is defined, only certain statements and other logic elements are meaningful and therefore valid. The valid statements and logic elements (variables, attributes, resources, distributions, operators, and scenario parameters) are available via the Keywords and Filters (Keywords & filters) dialog. These statements and elements may be pasted from the list box into the action window to help you construct the desired action logic.

Action Logic zoom window

You may also start typing your saved attributes, variable, scenarios, scenario parameters, activity, entity, resource names or any of the ProcessModel supported statements, functions or distributions to see a help menu popup to help you easily populate what you wish to add.

Auto help for action logic

Paste elements into the action window

1. Click the Keywords and Filters Keywords & filters button on the action logic window.

2. Select the type of element (including statements) you want from the pull down box.

3. Select the specific element or statement from the list box.

4. Press the Insert button to insert the element at the position of the cursor in the action edit window or simply double-click the item.

Important information to be aware of Action Logic often uses expressions (combinations of attributes, variables, numbers, and operators) in assigning values to attributes or variables, testing the value or state of a variable or attribute, etc. For more information on expressions and valid operators, see Expressions.

See Also To learn advance functions / Keyboard shortcuts, see “Advanced Options for Action Logic Window”.

3.11.1 – Functions

Functions provide you with critical system information at any given time during the simulation. They can be used in an assignment statement to set the value of a variable or attribute or in an IF…THEN statement to make a decision based on system information. There are several system functions: Clock(), Contents(), FreeCap(), FreeUnits(), GroupQty(), OwnedResource(), Percent(), and ResQty().

 System reserved words cannot be used as variable names, attributes, or scenario parameters.

Clock( )

Clock(min)
Clock(sec)
Clock(hr)
Clock(Day)
Clock(Wk)

Syntax:

CLOCK( ) or CLOCK(time unit)

Example:

a_Time = Clock( )
v_Time_in_System =
Clock( ) – CycleStart
If Clock(Hr) > 24
Then…

The default clock function returns the elapsed time of the simulation clock in minutes. It can be accessed, but not assigned a value. For example a variable could be assigned the value of the clock in minutes: v_Var1 = Clock() would result in the number of minutes elapsed in the simulation assigned to the variable. To assign the number of hours that have elapsed, use v_Var1 = Clock(hr), and to get the number of seconds, use v_Var1 = Clock(sec).

More Information

A common use of the Clock( ) function is collect and plot individual cycle times for entities. It can be used to help collect individual times in just part of the model or individual entities for the entire cycle time. Why would this be important? Doesn’t ProcessModel automatically collect and report the average cycle time for each entity type? Yes it does, but a major reason for simulation is to dig deeper than averages, because averages are misleading.

ProcessModel automatically notes the simulation clock time when each entity first enters the process and writes that information to a default entity attribute called CycleStart. You can use a formula below calculate and record the processing time for each entity.

Example: v_Time = Clock() – CycleStart

Each entity that crosses the action statement that contains this formula will find out the current simulation time, subtract the starting time of the entity and write the result to variable defined (in this case, v_Time).

Many times, however, it is helpful to know the time it takes for an entity to complete only a portion of the process, for example activities 4 to 6 of a larger process. In this case, you would enter a_attribute1 = clock() in the action logic of the route entering activity 4. [Not specifying a unit of time by not putting anything in the parentheses after “clock” causes ProcessModel to use the default time unit which is minutes.] This would cause ProcessModel to note the time that each entity enters activity 4. You would also enter v_variable1 = clock() – a_attribute1 in the action logic of the route leaving activity 6. This would cause ProcessModel to take a new clock reading, subtract from it the first clock reading, and write to a variable that reflected the difference which you could then track in your output reports.

Important information to be aware of If you change the default time units to hours, then the value returned will be divided by 60. If you change the default time units to seconds, then the value returned will be multiplied by 60.

Contents(name of activity) 

Returns the total number of entities at an activity. Use CONTENTS() to make decisions based on how busy an activity is. Using CONTENTS() to keep track of the number of entities at an activity requires fewer steps and allows more flexibility than using variables to count the number of entities that enter and exit an activity. For example, the second syntax does in one statement what would require several statements without the CONTENTS() function.

Example

A car wash has an activity called Wash that often gets too busy for one operator to handle so the supervisor then comes to help. The logic below models this situation with an IF…THEN statement and the CONTENTS() function. As long as the activity contains fewer than three cars, the worker processes any arriving car. However, if the contents of the activity are greater than three, the Supervisor may also be used.

IF CONTENTS(Wash) {

GET Worker

}
ELSE
{

GET Worker OR Supervisor

}

FreeCap(name of activity) 

Returns the available capacity of an activity (an integer).

Example

Suppose an entity can be routed to one of two identical ovens for a curing process. However, you would like to ensure the ovens are loaded as evenly as possible at all times. The following logic could be used to set an attribute (called a_Router) to a 1 or a 2 based on the available capacity of the ovens. Conditional routings would then be used to route to the appropriate oven:

IF FREECAP(Oven1) > FREECAP(Oven2) THEN
{

a_Router=1

}
ELSE {a_Router=2}

FreeUnits(name of activity or resource) 

Returns the free units of an activity or resource (an integer).

Example

The example below demonstrates the use of the FREEUNITS() function to adjust the processing time for an entity based on the number of units of a resource available to process it. Once a Plane arrives at a passenger gate, it captures a certain number of resources named BH (Baggage Handlers). Operation logic at the gate determines how many BH resources have been captured and, accordingly, how long it will take to service the Plane. The more Baggage Handlers a Plane captures, the less time it takes to service it.

WHILE FREEUNITS(BH) <3
{

DO TIME(2 min)

}
IF FREEUNITS(BH)>=5 THEN {GET 5 BH}
ELSE
{

GET FREEUNITS(BH) BH
TIME(60/RESQTY(BH) min)

}

GroupQty( ) 

Returns the number of entities in a batched or loaded entity (a loaded entity is an entity with other entities attached to it). If the entity is a loaded entity, it will return only the number of loaded entities, not the base entity. For example, if you attach four Castings to a Pallet, the GroupQty() will return the number of Castings (i.e. 4), which does not include the entity Pallet.

In the case of multiple levels of groups and loads, GroupQty() returns the number of entities in the uppermost level only.

Example

A group of documents called Folder arrives at the Secretary in-box and is processed for some amount of time according to the number of documents in the folder. Each document takes 3.0 minutes to process.

TIME (GROUPQTY() *3.0 min)

OwnedResource(n) 

Returns the n th resource currently being used by the entity. Each resource is referenced according to the order it was put into use so that the longest held resource is OwnedResource(1). The most recently captured resource can be referenced by omitting the number: OwnedResource().

Example

The OwnedResource() function is useful when a decision must be made based on the resource that was captured. For example, suppose an entity captures either Worker_1 or Worker_2 in order to perform an activity. If Worker_1 is used, the activity takes 5 minutes. If Worker_2 is used, the activity takes 6.5 minutes. This can be defined using the following Action logic.

GET Worker_1 OR Worker_2
IF OWNEDRESOURCE() = Worker_1 THEN {TIME(5.0 min)}
ELSE {TIME(6.5 min)}
FREE OWNEDRESOURCE()

Important information to be aware of This function cannot be assigned a resource name in Action logic. For example, an assignment statement like OwnedResource() = Worker_1 will generate an error.

Percent(n) 

The percent function allows you to execute one or more statements only a certain percentage of the time. Used in an IF…THEN statement, the function returns a TRUE or FALSE.

To use the percent function, enter PERCENT(n) where n is the percentage of the time that the statement will return a TRUE condition in an IF…THEN statement.

Example

In the first example, logic following the THEN statement is executed 21.5% of the time. In the second, logic following the THEN statement is executed 35% of the time and the logic following the ELSE statement is executed 65% of the time.

1) IF PERCENT(21.5) THEN…
2) IF PERCENT(35) THEN…

ELSE…

ResQty(name of resource) 

Returns the number of units of a specific resource that the current entity owns. You can use RESQTY() to determine the amount of time necessary to process an entity based on the number of units of a resource the entity owns.

Example

The example below demonstrates the use of RESQTY() to adjust the processing time for an entity based on the number of resources available to process it. Once a Plane arrives at a passenger gate, it captures a certain number of resources named BH (Baggage Handlers). Operation logic at the gate determines how many BH resources have been captured and, accordingly, how long it will take to service the Plane. The more Baggage Handlers a Plane captures, the less time it takes to service it.

IF FREEUNITS(BH)>=5 THEN {GET 5 BH}
ELSE
{

GET FREEUNITS(BH) BH

}
TIME(45/RESQTY(BH) min)

3.11.2 Statements

Statements are simply commands to be executed at particular stages in an entity’s progress through the process. The following pages explain in detail each statement listed below.

 System reserved words cannot be used as variable names, attributes, or scenario parameters.

Comments
( ) = ( ) assignment
ANIMATE
DEC
DISPLAY
FREE
GET
INC
IF…THEN…ELSE
JOINTLYGET
NEWGRAPHIC
NEWNAME
PAUSE
REPORT
STOP
TIME
WAIT UNTIL
WHILE…DO

Comments

Comments You can add explanatory comments to your action logic by placing special characters in front of the comment. Comment lines are for user information only and the simulation ignores them during run time. To include a single-line comment, use a pound sign “#” or two forward slashes “//” at the beginning of the line. Multiline comments begin with a “/*”… and end with a “*/”. Some statements and functions such as GET or FREE are not ignored when found in a single comment line. To ensure they are ignored, you must use the multiline /* . . . */ comment indicators, and the ending “*/” must appear on a different line than the opening “/*”.

 FREE, GET, and JOINTLYGET, PERCENT and GROUP statements are not ignored by the // comment indicator. Use the /* … */ comment indicators. These indicators are normally used to comment multiple lines. So if you are only commenting out a single line, the closing */ must appear on a second line since it cannot exist on the same line as the opening /*.

For example:

IF Test = Reject THEN INC RejectQty
/*Number of rejects increased
for each test reject.*/

or…

// The logic below describes how rejects are handled.

( ) = ( )

This is the assignment statement which allows you to assign a value (or descriptor) to a variable or to one of the attributes defined for your entities.

Syntax

assignee = assignor

assignee The variable or attribute to which the value is assigned.
assignor The value assigned to the variable. This could be another variable or attribute, a pre-defined descriptor, or a mathematical expression.

Example

In the first example, the attribute a_ Attr1 is assigned a value of 2. The second example assigns the value of a_ PO_No to the attribute a_ Invoice_No . Number three assigns the descriptor Red to the attribute a_ Color . And the last example assigns the product of 5 and the value of Base to the attribute a_ Size.

1) a_Attr1 = 2
2) a_Invoice_No = a_PO_No
3) a_Color = Red
4) a_Size = 5 * Base

ANIMATE

The ANIMATE statement allows the control of the animation speed through the action logic dialog.

Syntax

ANIMATE(speed)

speed A numeric value between 0 and 100. One is the slowest and one hundred is the fastest speed of animation. A speed of zero turns the animation off.

Example

In the first example, ANIMATE statement is used to turn the animation off so that the model will run ahead in time quickly. In the second example the speed of animation is set very slow (usually to show a particular entity or activity). In the third example the speed of animation is set to the highest possible speed with animation still on (often used to help the user gain a “feel” for the general flow and buildups that would occur over time.

1) animate(0)
2) animate(10)
3) animate(100)

The ANIMATE statement is usually implemented by placing a separate entity, arrival and activity combination in the existing model. Scheduled arrivals work well because the exact time can be assigned for the arrival(s) and individual action logic applies for each scheduled arrival. The first arrival could be used to “fast forward” to a time in the model of particular importance, while the next arrival could be used to slow the animation for inspection of a particular aspect of the simulation and then a third to again “fast forward” to see the results.

DEC

The decrement statement allows you to decrement a variable or attribute’s value. It subtracts one (the default) or more from the value of the variable or attribute.

Syntax

DEC name [, expression]

name This is the name of the variable or attribute to be decremented.

[expression] You can optionally decrement the variable or attribute by more than one using an expression which can be a constant or a mathematical expression. The name and expression must be separated by a comma. (The square brackets illustrate only that this element is optional.)

Example

The following are several easy-to-understand examples. The first decrements the value of v_ Var1 by one. The second decrements the value of a_ Attr1 by five. The third decrements the value of Number_in_System by the value of an attribute called a_ Batch_Size.

1) DEC v_Var1
2) DEC a_Attr1, 5
3) DEC Number_in_System, a_Batch_Size

DISPLAY

Pauses the simulation and displays a message. The simulation will resume when the user selects OK.

Syntax

DISPLAY “< text string >” [,< attribute / variable / function call >]
DISPLAY “Now completing the 100th set”
DISPLAY “The current number of entries is: “, Var1
DISPLAY “Var1 = “, Var1 $ CHAR(13) $ “Attr1 = ” $ Attr1

text string The message ProcessModel will display. The text string must be enclosed in quotes.

[attribute / variable / function call] The text string or numeric value you wish to display.

After the original set of information (i.e. text string, variable) the “$” character is used to add additional information (i.e another text string or variable). You can force a carriage return by using the statement CHAR(13). Each new item that is appended to the statement must be prefaced with the “$” character.

Example 1

This example displays a message whenever a new order type begins processing at the current activity. A variable, v_ Last_Order , stores the order type of the last entity processed at the activity. If the current entity’s a_ Order_Type attribute value is different from the previous order type, ProcessModel displays a message stating the new order’s type.

IF Order_Type <> Last_Order THEN
{

DISPLAY “New Order Type: ”, a_Order_Type
v_Last_Order = a_Order_Type

}

Example 2

This example displays the entity name during simulation.

Display “Entity Name = “, Name

Or

Display “Entity Name = “, Ent(Name)

Using just Name will display the entity number rather than the text name. Name is the system defined attribute which contains the numeric value of the entity name. Ent(Name) will display the text name of the entity.

Important information to be aware of The display statement is valuable for debugging complex models and for halting a model temporarily during a presentation to display information.

FREE

The free statement allows you to free a resource (or resources) being used by the current entity.

Syntax

FREE [ quantity ] resource
FREE [ quantity ] resource , [ quantity ] resource , …
FREE ALL

[quantity] The number of units of the following resource to free. If no quantity is used, the quantity is assumed to be one. (The square brackets illustrate only that this element is optional.)

resource The name of the resource or list of resource names to be freed. If any resource specified is not being used by the current entity, it is simply ignored.

ALL The keyword used with the FREE statement to free all captured resources.

Example

In the following example, an entity, which earlier captured the resource Operator, frees the Operator after a three minute activity time. This action is followed by an increment of the variable called v_TimesUsed.

TIME(3 min)
FREE Operator
INC v_TimesUsed

If no action statements follow the freeing of a resource, the resource can just as easily be freed by drawing a Free resource assignment connection between the resource and the activity.

GET

The get statement enables an entity to obtain a resource. ProcessModel attempts to capture the resources in the order they are listed. If multiple resources are requested, but not available, those that are available will be captured and tied up until all are available.

Syntax

GET [ quantity ] resource, [ priority ]
GET [ quantity ] resource, [ priority ] AND [ quantity ] resource, [ priority ]
GET [ quantity ] resource, [ priority ] OR [ quantity ] resource, [ priority]

[quantity] You can optionally specify the number of resources to get if the resource has multiple units defined for it. (The square brackets illustrate only that this element is optional.)

Important information to be aware of By default, quantity is equal to one unit of the resource.

resource The name of the resource to be captured.

AND Used to capture more than one resource as each becomes available. To wait until all become available before capturing any of them, use the JOINTLYGET statement.

OR Used to capture one resource or the other. Useful for situations where one of several resources could be used to accomplish the same thing.

[priority] You can optionally specify priority level to get the resource (0-99). The higher the number, the higher priority. A priority above 99 will become an interrupt priority, to learn more see Chapter 10, Section 10.5.8, Interrupting Resources. (The square brackets illustrate only that this element is optional.)

Example

The following examples demonstrate the use of the GET statement. The first shows a simple request for a resource called Operator . The second tests the Size attribute to determine whether or not the Operator and Helper are needed. And the third requests three units of the resource called Operator.

1) GET Operator
2) IF Size > 10 THEN GET Operator AND Helper
3) GET 3 Operator

Important information to be aware of Connected resources will always be captured before resources specified in a GET statement. On the other hand, resource connections that free a resource occur after any Action logic for the activity.

IF…THEN…ELSE

The if statement is used to test for a condition and then execute sections of logic based on whether that condition is True or False. The condition is described with a Boolean expression , If the condition is True, the logic flow branches one way. If the condition is False, the flow branches in another direction.

If-Then statements can be added to Action Logic fields of the following objects:

  • Arrivals
  • Activities
  • Storages
  • Routings

Syntax

If Boolean Expression Then
{

Statement

}

If Boolean Expression Then
{

Statement1

}
Else
{

Statement2

}

If Boolean Expression Then
{

Statement1
Statement2

}
Else
{

Statement3
Statement4

}

If Boolean Expression Then
{

Statement

}

If Boolean Expression Then
{

Statement1

}
Else
{

Statement2

}

If Boolean Expression Then
{

Statement1

}
Else
{

If Boolean Expression Then
{

Statement

}

}

conditional expression Is a comparative expression using comparison operators like the equals sign (=) and the less than/greater than symbols (< >). The result of this expression is either true or false (yes or no). Multiple or alternative conditions can be tested using the operators AND and OR. Parentheses may be used for nesting expressions.

statement_1 This statement is executed if the conditional expression is true. This can also be a block of statements started with a BEGIN keyword or symbol ({ ) and ended with the END keyword or symbol ( }).

statement_2 This statement, preceded by the keyword ELSE, is executed if the conditional expression is false. This can also be a block of statements started with a BEGIN keyword or symbol ({ ) and ended with the END keyword or symbol ( }).

Rules

  • All words must have at least one space separation
    NO IfName = Rework YES If Name=Rework OR If Name = Rework
  • If more than one action is the result of the If-Then statement, the group of statements must be bracketed By the Begin and End statements or {…}

    If Name = Phone_Call Then
    {

    Inc v_Calls
    Time(5 min)

    }
    Else
    {

    Inc v_Fax
    Time(10 min)

    }

    In the Example above if the Attribute “name” contains the word Phone_Call, then the statement is true. the statements after the THEN will be executed and all the statements after the Else will be skipped.

  • Indenting and carriage returns are optional — but if you want to be able to read your work next week, group blocks of statements and indent so that it is easy to see the result of true or false conditions.

Example

In the following examples the IF…THEN…ELSE statement is used to make decisions about what happens in the model. In the first example, the variable v_Calls is incremented if the Name has the same descriptor value as Phone_Call. The second example shows a decision made based on the a_Patient_Type. If the patient is critical, both a Nurse and a Doctor resource are needed. Otherwise, only a nurse is captured and the activity takes less time.

1)
IF Name = Phone_Call THEN INC v_Calls

2)
IF a_Patient_Type = Critical THEN
{

GET Nurse AND Doctor
TIME(N(30, 5) min)
FREE ALL

}
ELSE
{

GET Nurse
TIME(N(8, 1) min)
FREE ALL

}

INC

The increment statement allows you to increment a variable or attribute’s value. It adds one (the default) or more to the value of the variable or attribute.

Syntax

INC name [, expression]

name This is the name of the variable or attribute to be incremented.

[expression] You can optionally increment the variable or attribute by more than one using an expression which can be a constant or a mathematical expression. The name and expression must be separated by a comma. (The square brackets illustrate only that this element is optional.)

Example

The following are several easy-to-understand examples. The first increments the value of v_ Var1 by one. The second increments the value of a_ Attr1 by five. And the third increments the value of v_ Number_in_System by the value of v_ Num_Processed plus one.

1) INC v_Var1
2) INC a_Attr1, 5
3) INC v_Number_in_System, v_Num_Processed +1

JOINTLYGET

This statement allows an entity to get more than one resource, but not until they all become available.

Syntax

JOINTLYGET [ quantity ] resource, [ priority ]
JOINTLYGET [ quantity ] resource, [ priority ] AND [ quantity ] resource, [ priority ]
JOINTLYGET [ quantity ] resource, [ priority ] OR [ quantity ] resource, [ priority]

[quantity] Optionally specify the number of units of the resource that you want to capture. (The square brackets illustrate only that this element is optional.)

Important information to be aware of By default, quantity is equal to one unit of the resource.

resource The name of the resource you want to capture.

AND Used to capture more than one resource once they all become available. To capture multiple resources as each one becomes available, use the GET statement.

OR Used to capture one resource or the other. Useful for situations where one of several resources could be used to accomplish the same thing.

[priority] You can optionally specify priority level to get the resource (0-999). The higher the number, the higher priority. (The square brackets illustrate only that this element is optional.)

Example

The following examples demonstrate the use of the JOINTLYGET statement. The first shows a simple request for a resource called Operator and a resource called Helper. The second tests the a_ Size attribute to determine whether or not the Operator needs two Helper resources. The third example requests the Operator and three units of the resource called Helper . If three helpers are not available, the statement may get two operators and two helpers.

1) JOINTLYGET Operator AND Helper
2) IF a_Size > 10 THEN JOINTLYGET Operator AND 2 Helper
3) JOINTLYGET (Operator AND 3 Helper) OR (2 Operator AND 2 Helper)

NEWGRAPHIC

This statement allows you to change an entity’s graphic without changing the name of the entity. The NEWGRAPHIC statement will permit you to graphically depict a change in an entity’s state without affecting the statistics collected for the original entity. The NEWGRAPHIC statement allows depiction of:

• assembly, by adding to the complexity of graphics
• value or size change, by increasing the size of the graphics
• exception tracking, by changing the color of the graphic.

For more information, see Finding Graphic ID Numbers.

Syntax

NEWGRAPHIC(id#)

id# The identification number of the new entity graphic you want to use in the animation. Numbers (1-20) are pre-assigned to the entities in the standard entities shape palette. When you place entity graphics on the layout, ProcessModel adds them internally as graphic number 21, 22, 23, . . . and so on.

Example

Suppose you want a completed order to appear with a different graphic. To do this, you would use the NewGraphic statement for the entity (e.g. Order) to change its ID from 21 to 22 (this will substitute a new graphic for the original). At the activity or connection where the change takes place, enter the following statement in the Action logic:

NEWGRAPHIC(22)

NEWNAME

This statement allows you to change the name of an entity along with its graphic so that model animation as well as statistical reports will reflect the change. In effect, this statement reassigns the entity the name enclosed in parentheses and changes the graphic i.d. number appropriately.

Syntax

NEWNAME(name)

name The new name assigned to the entity with its accompanying graphic if defined on the layout. The new entity and its graphic must be previously defined on the layout when using this statement.

Example

We want to see completed orders as a different graphic and collect statistics on the completed orders, so we use the NewName statement on the entity named Order. At the activity or connection where the change takes place, enter the following in properties dialog Action logic:

NEWNAME(Completed_Order)

An entity can be renamed using either the New Name drop down list in a routing Properties Dialog Box, or the NEWNAME statement in action logic. When renaming, all attributes from the original entity are inherited except the system created attributes ID and Name. The system created attributes which are inherited are Cost, CycleStart, and VATime.

PAUSE

Pauses the simulation and (optionally) displays a user-specified message. This pause allows you to examine the system in detail by using menu items from the Options and Information menu. The simulation will continue only when the user selects Resume Simulation from the Simulation menu.

Syntax

PAUSE [< text string >]
PAUSE
PAUSE “Work in Process levels are critically low.”

text string The optional message ProcessModel will display.

Example

The simple example below pauses the simulation after the 100th Claim has been processed at activity Quality Check. The purpose for doing this might be to view the current state of the system at this particular point in time.

INC v_Total
IF v_Total >= 100 THEN
{

PAUSE “Total = 100”
v_Total=0

}

REPORT

Calculates and reports the current statistics to the output database. This is useful to get a snapshot of the model at various points during the simulation.

The REPORT statement may be followed by the WITH RESET option to reset the statistics after the report is made to the database. When you use the WITH RESET option, you generally want to provide some periodic looping event that will call the report function at specific times.

Used with the AS option, REPORT creates a report with the name specified in the expression.

Syntax

REPORT [WITH RESET] [AS < text string >]
REPORT
REPORT WITH RESET
IF v_thruput = 50 THEN REPORT AS “RepOvr50”

text string A unique name given to the report so it can be easily identified in the General Stats dialog in the Output Program. If any reports have the same name, a number is tacked on the end of the name to make it unique.

Example

To get a snapshot report every 40 hours, schedule a “dummy entity” to arrive periodically (every 40 hours) at a “dummy activity.” In the action logic field, enter the statement REPORT WITH RESET AS 40HOUR and set the activity time to 0.

Report syntax action logic

This results in reports named, 40HOUR, 40HOUR2, 40HOUR3, etc.

 Variables are not automatically reset. You will need to manually reset the variables with the report statement if they are being used to track statistics.

 When a REPORT statement is used in a model, the final report is not generated. So unless a Report statement occurs exactly at the end of the simulation, there will likely be some additional processing that occurs. But that last bit of processing won’t be reported. Make sure the last Report statement occurs exactly at the end of simulation time.

 The “reset” doesn’t reset the number of entities in the activity. So at the start of each new report, there could be entities already in the activity. Therefore, if we add the Total Entries values for each report, we will have a number greater than the actual number of entities that entered that activity. Rather than add the Total Entries values, increment a variable in the activity in question. Then view that variable in the output report.

STOP

Terminates the current replication and optionally displays a message. The simulation will then continue with the next replication. Use STOP to end a replication when a user-defined condition becomes true.

Syntax

STOP [<“ text string” >]
STOP
STOP “Normal termination”

text string An optional message to display when the replication stops.

Example

The example below uses a STOP statement to terminate the simulation whenever the variable v_Total_Complete reaches 100.

INC v_Total_Complete
IF v_Total_Complete = 100 THEN

STOP

TIME

The time statement allows you to require the entity to spend time at an activity and has the same effect as entering a time value under the General tab of the properties dialog. This permits you to define a processing time based on logical criteria such as the value of an entity attribute.

Syntax

TIME( time expression <wk/day/hr/min/sec >)

time expression The amount of time to detain the entity at the activity. It can also be expressed as a distribution to add variability to the processing time.

<hr/min/sec> The time unit for the expression.

Caution DO NOT use this statement in an arrival connection’s Action logic. Doing so will result in an error when you run the simulation.

Example 1

In the first example, a 5-second activity time is specified. In the second, the activity time is a normal distribution with a mean of 10 minutes and a standard deviation of 2.5 minutes. The third example illustrates a discrete distribution function where the activity has a 20% chance of taking 5 minutes, a 30% chance of taking 8 minutes, and a 50% chance of taking 10 minutes. The fourth example uses a mathematical expression to indicate an activity time in hours equal to 2.5 times the value of the a_ Size attribute.

1) TIME(5 sec)
2) TIME(N(10, 2.5) min)
3) TIME(D3(20, 5, 30, 8, 50, 10) min)
4) TIME(a_Size * 2.5 hr)

Important information to be aware of The time expression and time unit must be used together and separated by a space. A Day time unit equals 24 hours and Week time unit equals 168 hours.

Example 2

Using a Time statement with 0 time can be a very helpful piece of action logic. The purpose of this statement is to place the current entity at the bottom of the simulation’s processing stack. One application of this kind of statement is as follows. Suppose you want to have 20 items enter a storage, one at a time or in groups, and have all 20 items leave the storage at the same time. You can accomplish this with batching. However, you can simplify your model by using the following action logic in a storage.

Inc v_Counter
Wait Until v_Counter = 20
Time(0 Min)
Dec v_Counter

This action logic will count entities entering the storage, and hold them until there are 20. Once the Wait condition is true, all 20 entities move to the Time statement which will place each entity at the bottom of the simulation’s pending actions queue. The result is that the v_Counter variable is not decremented until all 20 entities have passed the Time statement. Since the processing sequence of all 20 entities are now consecutive, they all move one after the other to the Dec statement, reducing the counter to 0 before any new entities enter the storage. The process then repeats for the next 20 entities entering the storage.

WAIT UNTIL

Delays processing of the current logic until the Boolean expression is true. The rest of the model continues to process during the delay. Note that if the expression initially evaluates to be false, ProcessModel reevaluates it only when a variable in the expression changes. ProcessModel releases multiple entities waiting on the same condition one at a time. This allows a released entity to reset the variable value and prevent the release of other waiting entities.

Syntax

WAIT UNTIL
WAIT UNTIL v_Var1 > 3
WAIT UNTIL v_Var1 < a_Attr3 AND v_Var2 >= 5

Boolean expression The condition that must be satisfied to continue processing the entity or resource. One side of the Boolean expression must contain a variable that can change due to some other event in the model.

Example

The following example uses the WAIT…UNTIL statement to group a variable number of people at an airport shuttle stop. As each person arrives at the shuttle stop, a variable (v_Total) is incremented to keep track of the number of people waiting. The WAIT…UNTIL statement causes all people to wait at this point until the variable, v_Total, is equal to or greater than five.

INC v_Total
WAIT UNTIL v_Total >= 5

WHILE…DO

Repeats logic or logic block continuously while a condition remains true.

Syntax

WHILE DO
WHILE v_Var1 > 3 DO TIME(10 min)
WHILE v_Var1 < a_Attr3 AND Var2 >= 5 DO
{

}

Boolean expression The condition that must be satisfied to continue processing the entity or resource. One side of the Boolean expression must contain a variable that can change due to some other event in the model.

Example

The following example uses the WHILE…DO loop to track the day of the week. v_Var1 is a variable that is set to zero at the beginning of the simulation and remains constant, so the loop will continue for the length of the simulation. v_Day_of_Week is a variable that changes every 24 hours and is reset to one on the eight day.

While v_Var1 = 0 DO
{

INC v_Day_of_Week
IF v_Day_of_Week = 8 THEN v_Day_of_Week = 1
TIME(24 Hr)

}

3.12 – Expressions

An expression provides a value based on an evaluation of the names, constants, and symbols in the expression. Some expressions provide a numeric value; these are called numeric expressions. Others provide a true/false value; these are called Boolean expressions.

Expressions allow you to introduce variability into your model. They let you track, control, and respond to events. To create an expression you may use any combination of constants, probability distributions, attributes, and variables. These elements allow you to expand the scope and improve the credibility of your model.

This section shows you how to create expressions and how to use distributions, attributes, and variables in your model to accurately reflect the variability and randomness of the real world.

Numeric Expressions

Unless otherwise stated in this manual, expressions refer to numeric expressions which consist of elements (attributes, variables, distributions, and constants) combined with normal mathematical operators ( + , – , etc.) that result in a numeric value. A numeric expression may be as simple as a variable name or as complex as a formula.

Expression Elements

The following table illustrates the expression elements.

Element Meaning Examples
attribute name Attribute’s current value a_Weight
a_Pkg_Qty
a_SerialNumber
variable name Variable’s current value v_Num_In_System
v_Qty_To_Go
v_Total_Pieces
distribution A probability distribution T(5, 15, 45)
N(34, 3)
U(50, 5)
constant A specific number 5
10.25
19.01

Mathematical Operators

The following table illustrates the use of mathematical operators.

Item Meaning Examples
+ addition a_Weight + 2.5
a_Pkg_Qty + 3.4
subtraction v_Num_In_System – 5
100 – v_Qty_To_Go
* multiplication 5 * v_Total_Pieces
a_Weight * 0.98
/ division v_Total_Pieces / 100
a_Pkg_Qty / v_Bin_Qty
() parentheses (v_Total_Pieces – v_Total) / 100
a_Pkg_Qty / (v_Bin_Qty – 100)
** exponentiation a_Pkg_Qty ** v_Total_Pieces
2 ** v_Bin_Qty
modulus  a_Value1 = 10
a_Value2 = 4
a_Div = a_Value1 / a_Value2
v_Remainder = a_Value1 – (a_Div * a_Value2)// Where a_Div is an integer.

Examples of Numeric Expressions

You may combine items to form a compound expression. Parentheses may be used to set off parts of the expression to be evaluated first. For information on the order in which operators are evaluated to determine the expression’s value, see Operator Precedence below.

a_Attr1
50.91
v_Var1 + 5
v_Total_Pieces + 5 * a_Pkg_Qty
(a_Weight + 5) * (a_Pkg_Qty / 2)
N(25, 4.8) + a_Weight * (v_Total_Pieces – 10)

Boolean Expressions (True / False)

In addition to numeric expressions, you may use logical operators to create Boolean expressions that compare two numeric expressions yielding a result of True or False. These expression may be used in IF…THEN statements and condition fields to make specific decisions in the model based on the values of two numeric expressions.

Boolean Operators

The following table lists and illustrates the use of Boolean operators to create Boolean expressions.

Item Meaning Examples
= equal to a_Weight = 2.5
v_Total_Pieces = 50
> greater than a_Weight > 2.5
v_Total_Pieces > 50
< less than a_Weight < 2.5
v_Total_Pieces < 50
<> not equal to a_Weight <> 2.5
v_Total_Pieces <> 50
>= greater than or equal to a_Weight >= 2.5
v_Total_Pieces >= 50
<= less than or equal to a_Weight <= 2.5
v_Total_Pieces <= 50
AND both expressions a_Weight = 25 AND v_Total = 30
v_Total >= 20 AND v_Total <= 30
OR Done or both expressions a_Weight > 5 OR v_Total <= 20
v_Total = 30 OR a_Weight =15

Examples of Boolean Expressions

You may use simple or compound numeric expressions on either side of the Boolean operator.

IF v_Total_Pieces > 5 * a_Pkg_Qty THEN…

IF (a_Weight + 5) <= (a_Pkg_Qty / 2) THEN…

IF N(25, 4.8) + a_Weight = v_Total_Pieces – 10 THEN…

IF a_Weight >= v_Total_Pieces AND a_Pkg_Qty > 20 THEN…

IF v_Total_Pieces = a_Pkg_Qty OR a_Pkg_Qty > 35 THEN…

Operator Precedence

As in conventional mathematics, ProcessModel evaluates expressions with more than one operator according to certain rules of precedence. Expressions with more than one operator are evaluated from left to right in the following order:

1. Terms inside parenthesis: ( )
2. Multiplication: *; and Division: /
3. Addition: +; and Subtraction:
4. Equalities and Inequalities: =, <>, >, >=, <, <=

3.12.1 – Distributions

See Basics 7 – Why Variability is Critical video tutorial.

In the real world, events tend to occur randomly, according to certain statistical patterns or distributions. Distributions allow you to add randomness or variability to your model in order to make it more accurately reflect reality. ProcessModel is capable of creating random sample values that fit a specific theoretical or user-defined distribution.

Important information to be aware of You can use a distribution when specifying time values and quantities or when assigning a value to an attribute or variable. For example, this statement:

TIME (T(2, 2.6, 4) min)

would make the activity last for a time randomly selected from a triangular distribution with a minimum time of 2 minutes, a most likely time of 2.6 minutes, and a maximum time of 4 minutes.

Distribution functions are built-in functions that generate random values from numbers using pre-determined patterns. Distributions may be discrete, selecting one among a finite number of possible solutions, or continuous according to the pattern provided by the input parameters. An example of using a discrete distribution is when an entity can randomly route to one of several places, or when randomly generating a batch size. Examples of continuous distributions include service and inter-arrival times. The distributions directly available to you in ProcessModel are:

Distributions in ProcessModel simulation software

Many additional distributions can be used because of conversions made by Stat::Fit to ProcessModel distributions. See Convert Raw Data to Distributions.

Common Distributions

The following is a list of the most commonly used distributions and the syntax used to define them. The s parameter is optional (see discussion of streams following this list).

Common
Distributions
Syntax Components
Normal N(a, b, s) a = mean, b = standard deviation,
s = stream (optional)
example: N(30,5)
Triangular T(a, b, c,s) a = minimum, b=mode, c=maximum,
s = stream (optional)
example: T(2,10,13)
Uniform U(a, b, s) a = mean, b=half range, s = stream (optional)
example: U(20,4)
User-Defined D n (% 1 ,x 1 ,…% n ,x n ) % = percentage (entries must total 100%)
x = value (numeric or pre-defined descriptor)
n = number of%, x entries between 2 and 5
example: D3(20, 35, 30, 45, 50, 37.5)

The following illustration shows an example of each of the common distributions:

Common distributions in ProcessModel simulation software

Triangular This distribution is probably the most versatile yet easy to understand. It allows you to set a lower limit, an upper limit, and a most likely point. In the above example, the distribution would return a random number where the minimum value could be 2, the most likely value would be 5, and the maximum value could be 12.

For example, a call center may take as little as 1 minute with a call but usually takes 3.4 minutes and sometimes takes up to 15 minutes to complete the call. To model this in the Time field of the activity, you would enter: T(1, 3.4, 15)

Generally, you should express time values using the triangular distribution since it is flexible and realistic, yet easy to understand. Studies have shown that activity times are never uniformly distributed and rarely normally distributed. Generally, activity times approximate a triangular distribution that is skewed to the right as shown in the example above

Normal A normal Bell curve, this distribution allows you to designate a mean (average) and a standard deviation to generate a random number within that curve, the most likely number to be generated being the mean. The previous example shows a normal distribution with a mean or average of 30 and a standard deviation of 5.

Uniform This distribution allows you to generate a completely random number since any number under its curve is just as likely to be selected as any other. It is very useful when you want to have a random number unaffected by a most likely point. The previous example will result in a random number from 16 to 24 (i.e. 20 ± 4).

For example, suppose you want to assign a random number from 0 to 30 to an attribute called a_Cover_Time. Enter a_Cover_Time = U(15, 15) in the Action logic where the assignment needs to take place.

User-Defined This distribution is specialized to allow you to generate a specific number based on percentages. You can create two, three, four, or five possible outcomes. The previous example, D3(20, 35, 30, 37.5, 50, 45), will generate the number 35 exactly 20% of the time, 30% of the time the number will be 37.5, and 50% of the time 45 will be the number.

For example, two types of patients require different preparation times. About 38% of the time a nurse takes 25 minutes to prepare the patient and 62% of the time it takes 43 minutes. Enter D2(38, 25, 62, 43) in the Time field of the preparation activity.

To see the parameters for other distributions found in ProcessModel, select the Action tab for an Activity and select Distributions from the drop down filter. Select the desired distribution then select the Paste button to move a distribution into action field. This will show the parameters used to build up a distribution.

To determine what distribution to use in the absence of raw data, use the following table:

Characteristics Possible Distribution
Unbounded

symmetrical about the mean
unbounded distribution skewed to the left
unbounded distribution skewed to the right

Normal
Extreme Value IA
Extreme Value IB

Bounded above a minimum

Time to a random event — time between arrivals
Time to a complex event — failure times
Time to task completion — service times, repair times

Exponential
Gama
Gama

Bounded between and minimum and a maximum Time to task completion

— when the minimum is not more than 3 * (Max -Most likely)
— when the Maximum is not more than 3 * (Most likely – Minimum)
— Other

Triangular
Beta

Important information to be aware of For detailed information on all available distributions, use the Stat::Fit Help or the Stat::Fit user’s guide found in “C:\Program Files\ProcessModel\x.x\StatFit\SF Manual V2.pdf”.

Streams

An optional stream number (1 – 100), shown as the S in the above tables, can be used to generate independently random numbers for the distribution. If this option is omitted, ProcessModel will use stream 1. The range is 1 – 100. The stream number is used to start the random seed value of a distribution at a number other than 1, which is the default. The stream number, preceded by a comma, is included as an additional parameter at the end of a distribution. It is placed before the closing parenthesis.

Important information to be aware of Any negative values returned by a distribution that are used for a time expression will be automatically converted to zero.

3.12.2 – Convert Raw Data to Distributions

See Stat::Fit video tutorial.

SummaryStat::Fit Inputting Data

1. Click Stat::Fit from the Tools menu.

2. Copy the column of data that you want to use within Stat::Fit.

3. Click in the open space directly across from the number 1 and press CTRL + V to paste data.

Stat::Fit Window with Data Pasted

4. Click the Auto::Fit button.

5. In the Auto::Fit dialog, select unbounded, lower bound, or assigned lower bound and click OK.

Important information to be aware of Users with greater statistical background may enjoy experimenting with the power of Stat::Fit here.

Stat::Fit Distributions Window

6. Click on the distribution you wish to use in the dialog displayed. This will generate the Comparison graph.

Stat::Fit Graphs Window

Important information to be aware of The Comparison graph allows you to compare your actual data against the selected distribution. The bars represent your data, while the line represents the distribution that will fit that data.

Important information to be aware of Other comparative graphs are accessible through the Results option of the Fit menu.

7. With the best distribution selected, click the Export button. Click OK to export the distribution to the clipboard.

Stat::Fit Export Distribution Window

8. Now return to ProcessModel and paste the distribution in the desired field or Action logic using the CTRL-V shortcut.

Detailed Information

Stat::Fit® is a comprehensive yet user-friendly curve fitting package. Stat::Fit will take raw data from spreadsheets, text files, or manual input and convert that data into the appropriate distribution for instant input into ProcessModel software.

It automatically fits continuous distributions, compares distribution types, and provides an absolute measure of each distribution’s acceptability. It also translates the fitted distribution into specific forms for use in ProcessModel products. It is developed by our technology partners at Geer Mountain Software.

Stat::Fit statistically fits your data to the most useful analytical distribution. Its operation is intuitive, yet its help file extensive. The Auto::Fit function automatically fits continuous distributions, provides relative comparisons between distribution types, and an absolute measure of each distribution’s acceptability. The Export function translates the fitted distribution into ProcessModel. Some of the features are included below

Stat::Fit takes raw data (e.g. collected service times) and turns them into a single distribution that represents the collected data. For example, data collected on the length of breakdowns can be turned into a single distribution and be placed in a ProcessModel field.

Using stat fit with processmodel

Stat::Fit is accessed from the Tools menu. It allows you to improve the accuracy of your models by using collected data to determine the best distribution to use in order to reflect that data (See “Distributions” in Chapter 3.12.1).

Distribution Fitting:

For Many distributions:

Beta, Binomial, Chi Squared, Erlang, Exponential, Extreme ValueIA, Extreme Value IB, Gamma, Geometric, Inverse Gaussian, Inverse Weibull, Johnson SB, Johnson SU, Logarithmic, Logistic, Loglogistic, Lognormal, Normal, Pareto, Pearson V, Pearson VI, Poisson, Power Function, Rayleigh, Triangular, Uniform, Weibull.

Descriptive Statistics:

Mean, Median, Mode, Standard Deviation, Variance, Coefficient of Variation, Skewness, Kurtosis.

Parameter Estimates:

Maximum Likelihood, Moments.

Goodness of Fit Tests:

Chi-squared, Kolmogorov-Smirnov, Anderson-Darling.

Graphical Analysis:

Density graphs, Distribution Graphs, Difference graphs, Box Plots, Q-Q plot, P-P plot, Scatter plot, Autocorrelation graphs.

Additional Features:

Built-in random variate generator, Data manipulation options, Distribution Viewer, Distribution Percentiles

Continuous Distributions vs. Discrete Distributions

Distribution fittings are built-in functions that generate random numbers using predetermined patterns. Distributions may be discrete, randomly returning one value among a specified list of values, or they can be continuous and interpolate randomly according to the pattern provided by the input table or parameters. There are several steps in determining the best distribution to use given raw data from observations of the process being modeled. First, you must determine whether the data is discrete or continuous, then follow the appropriate instructions. For instructions on finding the best discrete or continuous distribution, see Discrete distribution.

Stat::Fit is capable of much more than fitting data to distributions, but you need only take advantage of a few of its easy-to-use features when fitting your data to a ProcessModel distribution.

Continuous Distribution

The following example shows you how Stat::Fit can help you create more accurate models. A bank wants to model its teller operations, including the amount of time that it takes to serve each customer. Therefore, for a week, the time each customer spent with a teller is recorded. The data is entered in a text file which can be read by Stat::Fit. Using Stat::Fit, the data is analyzed and an activity time distribution is found that accurately reflects the amount of time required to serve a customer.

Discrete Distribution

The following example shows how a restaurant could use ProcessModel to model its seating operation. The number of customers is a quantity of discrete entities. Therefore, the Stat::Fit component of ProcessModel would take data about the number of customers who enter in each group, create a discrete distribution to represent that data, and place the distribution in the Quantity field for Arrivals in the ProcessModel for the restaurant.

Determine the best discrete distribution from raw data

1. Click Stat::Fit from the Tools menu.

2. Copy the column of data that you want to use within Stat::Fit.

3. Click in the open space directly across from the number 1 and press CTRL + V to paste data.

Discrete distribution example

4. Click the Auto::Fit button Auto fit button in stat fit.

5. In the Auto::Fit dialog , select discrete distributions as shown below and click OK.

Fitting discrete distribution

Stat::Fit then calculates the best distribution choices and displays them along with their rank (the higher the rank, the better the fit)

Automatic fitting by Stat Fit

6. Click on the name of the distribution that best fits the data.

Finding the best discrete distribution

7. With the best distribution found, follow the instructions for exporting data to ProcessModel found in fitting continuous data.

Replications in Stat::Fit

Replications in Stat::Fit

Replications in Stat::Fit

Find out how many process simulation replications should be run in order to accurately represent a system. There are formulas to calculate replications required, even better one of the formula’s has been automated in Stat::Fit, a fantastic tool provided with ProcessModel. Stat::Fit is found on the Tools/Stat::Fit menu.

The Replications command allows the user to calculate the number of independent data points, or replications, of an experiment necessary to provide a given range, or confidence interval, for the estimate of a parameter. The confidence interval is given for the confidence level specified, with of default of 0.95. The resulting number of replications is calculated using the t distribution.

The expected variation of the parameter must be specified by either its expected maximum range or its expected standard deviation. Quite frequently, this variation is calculated by pilot runs of the experiment or simulation, but can be chosen by experience if necessary. Be aware that this is just an initial value for the required replications, and should be refined as further data are available.

Alternatively, the confidence interval for a given estimate of a parameter can be calculated from the known number of replications and the expected or estimated variation of the parameter.

Distributions and their Usage

Following is a list of distributions and their general uses in ProcessModel. These distributions can be determined by using Stat::Fit and then used within different areas of ProcessModel. For a complete list of usable distributions and their descriptions, please refer to the Appendix of the Stat::Fit user’s guide found at: C:\Program Files\ProcessModel\x.x\StatFit\SF Manual V2.pdf.

Distributions Usage
Beta The Beta distribution is a continuous distribution that has both upper and lower finite bounds. Because many real situations can be bounded in this way, the Beta distribution can be used empirically to estimate the actual distribution before much data is available. Even when data is available, the Beta distribution should fit most data in a reasonable fashion, although it may not be the best fit.

For more information, see Beta Distribution.

Binomial The Binomial distribution is a discrete distribution bounded by [0,n]. Typically, it is used where a single trial is repeated over and over, such as the tossing of a coin. The parameter, p, is the probability of the event, either heads or tails, either occurring or not occurring. Each single trial is assumed to be independent of all others. The Binomial distribution can be used to represent the sampling of defective parts in a stable process, and other event sampling tests where the probability of the event is known to be constant or nearly so.
Exponential The Exponential distribution is continuous and is frequently used to represent the time between random occurrences, such as the time between arrivals at a specific location in a queuing model or the time between failures in reliability models. It has also been used to represent the services times of a specific operation.
Erlang The Erlang distribution is a continuous distribution bounded on the lower side. The Erlang distribution has been used extensively in reliability and in queuing theory.
Gamma The Gamma distribution is a continuous distribution bounded at the lower side. The Gamma distribution has been used to represent lifetimes, lead times, personal income data, and service times.
Geometric The Geometric distribution has been used for inventory demand, marketing survey returns, etc.
Inverse Gaussian The Inverse Gaussian distribution is a continuous distribution with a bound on the lower side. This distribution can be used to represent reliability and lifetimes, and repair time.
Lognormal The Lognormal distribution is a continuous distribution bounded on the lower side. This distribution can be used to model the duration of sickness absence, physicians’ consultant time, lifetime distributions in reliability, distribution of income, employee retention, and many applications modeling weight, height, etc.

For more information, see Lognormal Conversion.

Normal Normal distribution is frequently used to represent symmetrical data, but suffers from being unbounded in both directions. If the data is known to have a lower bound, it may be better represented by suitable parametrization of the Lognormal, Weibull, or Gamma distributions. See distributions for a description of the input parameters.
Poisson The Poisson distribution is a discrete distribution bounded at 0 on the low side and unbounded on the high side. The Poisson distribution finds frequent use because it represents the infrequent occurrence of events whose rate is constant. This includes many types of events in time or space such as arrivals of telephone calls, defects in semiconductors manufacturing and defects in all aspects of quality control.
Pearson5 The Pearson 5 distribution is a continuous distribution with a bound on the lower side. The Pearson 5 distribution is useful for modeling time delays where some minimum delay value is almost assured and the maximum time is unbounded and variably long, such as time to complete a difficult task, time to respond to an emergency, time to repair a tool, etc.
Pearson6 The Pearson 6 distribution is a continuous distribution bounded on the low side.
Triangular The Triangular distribution is a continuous distribution bounded on both sides.The Triangular distribution is often used when no or little data is available; it is a good starting point for approximating data, but it is rarely an accurate representation of a data set. (see Law & Kelton1). See distributions for a description of the input parameters.

For more information, see Triangular Distribution.

Uniform The Uniform distribution is a continuous distribution bounded on both sides. Its density does not depend on the value of x. The Uniform distribution is used to represent a random variable with constant likelihood of being in any small interval between min and max. See distributions for a description of the input parameters.

For more information, see Uniform Distribution.

User-Defined The User-Defined distribution is a discrete distribution defined by a probability of obtaining each value. See distributions for a description of the input parameters.
Weibull The Weibull distribution is a continuous distribution bounded on the lower side. In particular, the Weibull distribution is used to represent wear out lifetimes in reliability, health related issues, and duration of industrial stoppages.

3.12.3 – Attributes

Attributes are values or placeholders associated with individual entities that may, for example, indicate the entity’s size or condition. Attributes are placeholders for either values (real or integer) or descriptors (single-word descriptions). An attribute’s value can only be assigned, incremented, decremented and examined by the entity to which the attribute belongs. For example, while ActivityA processes an entity, the attribute called a_Color could be tested for only that particular entity in the activity’s Action Logic. Entities have the following pre-defined attributes:

Name – The name of the entity. DO NOT use an assignment statement to assign this attribute, e.g., Name = BadCall will not work. Use the NewName statement or the available fields in the routing properties dialog.
Cost – The current accumulated cost for an entity. To learn more about costs, see ProcessModel and Activity-based Costing.
VATime – Cumulative value-added time (in minutes).
ID – Unique identifying number assigned to each entity (created entities have the same ID as the entity that created them so they can be reunited later if desired).
CycleStart – Time (in minutes) entity entered system.
BVATime – Time (in minutes) added to BVA change.
GraphicID – Unique identifying number assigned to each entity graphic
ProcessStartTime – Process start time (in minutes).
CreatedQty – Number of entities created.
LastResTime – Time spent by the last resource on the entity (in minutes).
ReBatchQty – Previous batch quantity.
LoadStatus – Displays the load status of an entity when using an attach route. A value of 1 means all the entities needing to be attached are now attached, a 0 means that the entity is still waiting for the entities to attach or no attach is being used. LoadStatus will only store a value of either 0 or 1.
RoutingBlock_ – Entity Attribute for each subchart.

Pre-defined attributes are manipulated automatically by the system, so they should only be manually manipulated with careful forethought.

Creating User-Defined Attributes

User-defined attributes are defined by the user in the Attributes & Variables dialog accessed from the Insert menu. User-defined attributes may be given an initial value using action statements defined for the entity arrival.

Creating User-Defined Attributes

Shown here after a new descriptive attribute was created using the New button.

New This button creates a new attribute.

Delete This button deletes the selected attribute. (Note that the pre-defined attributes may not be deleted.)

Name The name of the attribute. Letters, numbers, and the underscore “ _ ” character are allowed in the name. The name must be one continuous word (i.e. a_Priority_Level).

Type The type of attribute. This can be set to Integer, Real, or Descriptive.

If you assign a real value (containing a decimal portion) to an integer parameter (user or system defined), the value will be truncated to 0 decimal places. It will not be rounded. Capacity, input queue, output queue, batch size, and resource quantity fields are all integer fields which will be truncated if you enter a number with a decimal value.

Integer Any whole number (no digits to the right of the decimal).

Real Any number including those with digits to the right of the decimal. Use Real when a high level of accuracy and detail is needed.

Descriptive Defined with a list of adjectives or descriptors that may be assigned to the attribute.

Paste Rule How the Attribute will be added when pasted into a model.

Duplicate If the Attribute does not exist in the target model, then it will be added to the Attribute dialog. If the Attribute does exist in the target model then that Attribute will not be added to the Attribute dialog.

Clone If the Attribute does not exist in the target model, then it will be added to the Attribute dialog. If the Attribute does exist in the target model then it will be added with post-fix (a_color will become a_color1). In addition, all instances of the a_color in the logic to be pasted will become a_color1.

Descriptor list The list of adjectives or descriptors that may be assigned to the descriptive attribute. Only available for attributes whose Type is Descriptive.

Using Descriptors

Attributes and Variables can be defined with a type: Descriptive. The Descriptor List is where a list of adjectives / descriptors that may be assigned to the descriptive attribute or variable can be defined. Descriptors are helpful in making your model easier to read and understand. Difficult logic is easier to explain to others when using descriptive attributes and variables.

For most models, few rules are needed when defining descriptive attributes and variables. However, if a model is developed that transfers the value of descriptive attributes or variable to other descriptive attributes or variables then additional care is needed during definition. When declaring Descriptors for attributes and variables the following rules should be considered:

  1. Reserved words should not be used.
  2. Should not start with a number.
  3. Descriptors that are common between multiple attributes or variables should always be defined in the same order, in all the attributes and variables. Common descriptors must be listed one after the other. The initial value for all variables that use common descriptors must be set and must be the same.
  4. It is recommended that a space is not added in the descriptor names, any space added will result in it being replaced with an underscore ‘_’ during simulation.

Examples:

Invalid Descriptors:

  1. CONTENTS
  2. CREATE
  3. CYCLESTART
  4. D2
  5. D3
  6. 14THSTATION
  7. 17THRESOURCE

Note: In the descriptors above 1 to 5 are reserved words, while 6 and 7 use a number at the start.

Valid Descriptors:

  1. CONTENTSOF1
  2. CREATEFROM1
  3. CYCLESTARTOF1
  4. D2FROMTO1
  5. D3FROMTO1
  6. STATION14TH
  7. RESOURCE17TH
  8. COLORRED
  9. YELLO
  10. YES
  11. NO

Invalid Same Descriptors:

  1. a_Test1:
    • RED
    • GREEN
    • BLUE
    • YELLOW
    • BLACK
  2. a_Test2
    • BLUE
    • GREEN
    • RED
    • BLACK
    • YELLOW
  3. v_Test1
    • GREEN
    • RED
    • BLUE
    • YELLOW
  4. v_Test2
    • BLUE
    • RED
    • GREEN
    •  BLACK

Valid Same Descriptors:

  1. a_Test1:
    • RED_1
    • GREEN_2
    • BLUE_3
    • YELLOW_4
    • BLACK_5
  2. a_Test2
    • RED_1
    • GREEN_2
    • BLUE_3
    • YELLOW_4
    • BLACK_5
  3. v_Test1
    • RED_1
    • GREEN_2
    • BLUE_3
    • YELLOW_4
    • BLACK_5
  4. v_Test2
    • RED_1
    • GREEN_2
    • BLUE_3
    • YELLOW_4
    • BLACK_5

Note: The value of a descriptive variable, when using the DISPLAY action logic will return an integer value based on the descriptors position in the descriptor list, the _# at the end of each descriptor will help in quick troubleshooting.

Define an Attribute

1. Select Attributes & Variables from the Simulation menu.

2. Press the New button.

3. Enter the name of the attribute.

4. Select the type: Real, Integer, or Descriptive. If you select Descriptive, you must enter the list of descriptors in the edit box provided. (Each descriptor should be entered on a separate line.)

Hierarchical Model – Delete Attributes

Linked hierarchical model files synchronize data when you save each file. When you delete a variable, attribute, or scenario parameter in one model file, it is added back in from the other linked files during synchronization.

  1. Unlink your hierarchical model files.
  2. Make your change in each file and save it.
  3. Re-link the hierarchical structure

3.12.4 – Variables

Like attributes, variables are placeholders for either values (real or integer) or descriptors (single-word descriptions). They may be used to describe or track activities and states in the system such as the number of entities that have completed a particular activity. Variables are global in nature and can be set, incremented, decremented, and examined in the Action tab of the properties dialog (most elements have an action tab). Variables are of two types: predefined and user-defined. Predefined variables that are set up automatically include the following:

  • Qty_Processed_ Number of entities processed for an entity type. For example, Qty_Processed_EntA is the number of EntA processed.
  • Avg_VA_Time_ Average value-added time (time units) for an entity type. For example, Avg_VA_Time_Orders is the number of time units the entity type Orders has spent in value-added activity.
  • Avg_Cycle_Time_ Average cycle time (time units) for an entity type. For example, Avg_Cycle_Time_AssemblyA is the average number of time units that the entity type AssemblyA spent in the model.
  • Avg_Cost_ Average cost for an entity type. For example, Avg_Cost_Call is the average cost for the entity type Call in the model. Average cost applies only to completed entities.

 The largest variable values ProcessModel can use is: 2,147,483,647.

Variables may also be displayed during the simulation on the scoreboard or in a user defined position.

User Defined Variable

Creating User-Defined Variables

You can create user-defined variables in the Attributes & Variables dialog accessed from the Insert menu. You can also give the variable an initial value.

Attributes & Variables

Shown here after a new Integer variable was created using the New button.

New This button creates a new variable.

Delete This button deletes the selected variable. (Note that the pre-defined variables may not be accessed from the Attributes & Variables dialog.)

Name The name of the variable. Letters, numbers, and the underscore “ _ ” character are allowed in the name. The name must be one continuous word (i.e. v_Inventory_Level).

Type The type of variable. This can be set to Integer, Real, or Descriptive.

If you assign a real value (containing a decimal portion) to an integer parameter (user or system defined), the value will be truncated to 0 decimal places. It will not be rounded. Capacity, input queue, output queue, batch size, and resource quantity fields are all integer fields which will be truncated if you enter a number with a decimal value.

• Integer Any whole number (no digits to the right of the decimal).

• Real Any number including those with digits to the right of the decimal.

• Descriptive Defined with a string of adjectives or descriptors that may be assigned to the variable.

For more information, see Using Descriptors.

Initial Value The value assigned to the variable at the beginning of the simulation. If you do not enter a value or descriptor, ProcessModel will use zero (0) or the first descriptor in the list as the initial value for the variable.

Stats Changes the type of statistics that are collected.

None No statistics will be collected

Basic Collects basic statistics such as total changes, average minutes per change, current value and average value. Observation based.

Basic Time Same information as Basic, but the information is Time-weighted.

Detailed Allows all of the same information as the Basic option plus standard deviation information.

Detailed Time Same information as Detailed, but the information is Time weighted.

Observation-based Variable information is calculated based on a simple average.

Time-weighted Variable information is calculated based the average of the products of the variable multiplied by the length of time it remained at that value.

Paste Rule How the Global Variable will be added when pasted into a model.

Duplicate If the Global Variable does not exist in the target model, then it will be added to the Global Variable dialog. If the Global Variable does exist in the target model then that Global Variable will not be added.

Clone If the Global Variable does not exist in the target model, then it will be added to the Global Variable dialog. If the Global does exist in the target model then it will be added with post-fix (v_Reject_Count will become v_Reject_Count1). In addition, all instances of the v_Reject_Count in the logic, to be pasted, will become v_Reject_Count1.

Scoreboard The variable will be displayed on the simulation scoreboard when this object is checked.

Descriptor list The list of adjectives or descriptors that may be assigned to the descriptive variable. Only available for variables whose Type is Descriptive.

Define a variable

1. Select Attributes & Variables from the Insert menu.

2. Click on the Global Variables tab.

3. Click on the New button.

4. Enter the name of the variable.

5. Select the type: Real, Integer, or Descriptive. If you select Descriptive, you must enter the list of descriptors in the box provided with a separate descriptor entered on each line.

6. Enter an initial value.

Display a Variable on the Scoreboard

1. Select Attributes & Variables from the Insert menu.

2. Click on the Global Variables tab.

3. Select the Scoreboard check box.

Display a variable in a user defined position

1. Select the Label palette from the Gallery.

Selecting the Label palette

2. Select a Label graphic and place it on the layout to the left of where you want the variable to be displayed.

3. In the label dialog box select the variable name to be displayed on-screen.

Displaying a user defined variable through a label

Hierarchical Model – Delete Variables

Linked hierarchical model files synchronize data when you save each file. When you delete a variable, attribute, or scenario parameter in one model file, it is added back in from the other linked files during synchronization.

  1. Unlink your hierarchical model files.
  2. Make your change in each file and save it.
  3. Re-link the hierarchical structure