Quisim Block API Reference

Overview

This reference lists every block of the Quisim modeling language with its attributes, sockets and measures. It complements the Quisim Simulation Modeling Handbook, which explains the concepts (entities, blocks, pipes and wires) and walks through complete models. Terms such as replication, renege or warm-up are explained in the Glossary.

Note: This page is generated automatically from Quisim itself. It describes Quisim 0.22 (16 Oct 2026).

Contents

• How to Read an Entry
• Attribute Types
• Common Attributes
• Entity Attributes
• Connectors
• Entity Blocks
  • Arrive — Creates entities at outlet with interarrival times given by duration.
  • Provide — Provisions an entity at outlet at all times, indefinitely.
  • Leave — Deletes any entity incoming at the inlet.
  • Perform — Delays any entity incoming at the inlet by the given duration.
  • Queue — Temporarily stores entities incoming at the inlet.
  • Meter — Counts the number of passed entities from inlet to outlet.
  • Decide — An incoming entity is sent to outlets[S] where S is the selectedOutlet number, which is always rounded down to the next integer.
  • Select — The incoming entity is taken from inlets[S] where S is the selectedInlet number, which is always rounded down to the next integer.
  • Pack — Packs entities incoming at the inlet into the packet entity.
  • Unpack — Unpacks a packet entity incoming at the inlet.
  • Set — Sets the attribute named attribute of a passing entity to value.
  • Get — Gets the value of attribute named attribute of an entity that is being evaluated for being passed through.
  • Lineup — Synchronizes multiple entities.
• Value Blocks
  • Explorer — Explores simulation runs in a loop by configuring and evaluating them.
  • Note — Displays a text note.
  • Number — Shows a number, either fixed or from the inlet, and provides it at the output.
  • NumberInput — Editable number that is provided at the output.
  • Function — Calls the stated function code, reading the parameter array P and writing to the measure array M.
  • Aggregate — Reduces incoming value to an aggregate value, initialized with init.
  • BrentOptimize — The Brent algorithm optimizes the given objective locally in the range [lower,upper].
• Random Number Blocks
  • RandomUniform — Generates uniformly distributed random variates in the range [lower,upper).
  • RandomExponential — Generates exponentially distributed random variates adhering to the given mean µ, which is the inverse of the rate λ.
  • RandomPoisson — Generates Poisson distributed random variates adhering to the given mean µ.
  • RandomDiscrete — Generates discrete random variates: outcome i is drawn with probability probabilities[i] and yields values[i], or i itself if values is empty, i.e., 0,1,…,n-1 for n probabilities.
  • RandomContinuous — Generates continuous random variates from an empirical distribution by the inversion method: the cumulative distribution function F is linear between support points, and each variate is F⁻¹(u) for a uniform u.
  • RandomPiecewiseLinear — Generates continuous random variates whose density is piecewise linear: values are k+1 non-decreasing points and densities the density at each point, linear in between and normalized by the total area.
  • RandomGamma — Generates gamma distributed random variates adhering to the given shape k and scale θ.
  • RandomWeibull — Generates Weibull distributed random variates adhering to the given shape k and scale θ.
  • RandomNormal — Generates normally distributed random variates adhering to the given mean µ and standard deviation stddev σ.
  • RandomLognormal — Generates log-normally distributed random variates adhering to the given mean µ and standard deviation stddev σ.
  • RandomPERT — Generates PERT distributed random variates in the range [lower,upper] for an expert estimate: at least lower, most likely mode, at most upper, where lower≤mode≤upper.
• Report Blocks
  • Chart — Shows a stairstep line chart of the given input parameters P.
  • Histogram — Displays a histogram of the given input parameters P.
  • Report — Collects a protocol of the given input parameters P.
  • Statistics — Computes mean values and confidence intervals of the given input parameters P.
  • SummaryStatistics — Estimates the mean and confidence interval of a sample of results, such as the batch means M of a Statistics block wired into its input V.
  • EnsemblePlot — Displays a Welch plot of the given input parameters P, used to identify the warm-up (transient) phase before steady state.
• Steering
• Glossary

How to Read an Entry

Each block section starts with the description shown in Quisim's info tab, followed by a short example in the modeling language and up to five tables. Tables that would be empty are left out.

• Attributes are the properties you set inside the braces of a block, for example Queue { id: queue0; capacity: 5 }. Default is the value used when you set nothing (— means there is no fixed default; “same as x” means the default follows attribute x). If an attribute also has a Wire input, its value can instead be driven during the run by a wire into that connector.
• Entity inputs and Entity outputs are the green entity sockets: inputs on the left, outputs on the right. Their connector names are the endpoints of a Pipe.
• Data inputs are the silver data sockets on the left. Their connector names (parameter…) are the targets of a Wire. An input marked wire only has no attribute that could be set as a constant.
• Measures are the silver data sockets on the right, the values a block reports. Their connector names (measure…) are the sources of a Wire.
• Label is the short symbol drawn next to a socket in the editor, such as C, L or RD. Labels never appear in model code; use the Connector name instead.
• Name (measures only) is the descriptive name of a measure, which is usually also a property of the block, such as load for queue0.load. A Wire still uses the Connector name. The column is left out when no measure of the block has a name.

A constant is assigned by the attribute name, as in Queue { id: queue0; renegeDuration: 60 }. To vary the value during the run, a wire goes into the data input of that attribute instead, named by its connector, here queue0.parameterRenegeDuration. For a given attribute, use either a constant or a wire, not both.

Indexed connectors

Some blocks have a variable number of sockets. These form zero-indexed arrays that are written inlets[0], outlets[1], parameters[0] or measures[2] in the modeling language. In the editor the same sockets are labelled I0, O1, P[0] or M[2]. The number of sockets is set by a count attribute such as numParameters, numOutlets (Decide), numInlets (Select), numConnectors (Lineup) or numMeasures (Function, Explorer); raise it before you connect a further index. See also Block Sockets in the handbook.

Inside the code of a Function block (its code, context and init attributes) and of an Explorer block (its code attribute), P and M are the JavaScript arrays themselves, so M[0]=P[0]+P[1] is correct there. The code of an Aggregate block uses A (the aggregate) and V (the incoming value) instead, as in A+=V.

Statistics column

The Statistics column of the measures table tells how a downstream Statistics block should aggregate the measure:

• time-weighted: a level that varies over time, such as a queue length. Set timeAverage: true on the Statistics block.
• cumulative: a running total, such as a count of passed entities. Set cumulative: true to report its increase over the run.
• —: a plain value per observation. Leave both off.

Special values

In the tables, ∞ stands for Infinity (unlimited), −∞ for -Infinity, and NaN for “not a number”, which several blocks use to mean “determine automatically”. In model code, write Infinity, -Infinity or NaN, for example Perform { id: perform0; capacity: Infinity }.

Attribute Types

Every attribute and data input has a type that determines which values Quisim accepts. Quisim checks the values you enter in the properties pane against this type.

Type	Meaning	Examples
PositiveInt	Whole number ≥ 1 (ℕ⁺).	1, 2, 100
NonNegativeInt	Whole number ≥ 0 (ℕ₀).	0, 1, 42
Int	Whole number, may be negative (ℤ).	-3, 0, 7
Real01	Number strictly between 0 and 1, i.e. in the open interval (0, 1).	0.05, 0.5
Real01Closed	Number from 0 to 1 inclusive, i.e. in the closed interval [0, 1].	0, 0.95, 1
Real	Any number, including Infinity and -Infinity.	-2.5, 1e6, Infinity
FiniteReal	Any finite number (no infinity).	-2.5, 0, 120
PositiveReal	Number > 0; Infinity is allowed.	0.5, 100, Infinity
NonNegativeReal	Number ≥ 0; Infinity is allowed.	0, 12.5, Infinity
NonNegativeFiniteReal	Finite number ≥ 0.	0, 12.5
PositiveFiniteReal	Finite number > 0.	0.5, 40
PositiveIntWithInfinity	Whole number ≥ 1, or Infinity for “unlimited”.	1, 5, Infinity
NonNegativeIntWithInfinity	Whole number ≥ 0, or Infinity for “unlimited”.	0, 5, Infinity
Bool	Truth value.	true, false
Id	Identifier of a block: a letter or _, followed by letters, digits or _.	queue0
Variant	Any value: number, truth value, string or array.	3, true, "red"
ReadOnly	Set by Quisim; can be read but not assigned.	—
Array	List of values.	[1, 2, 3]
Function	JavaScript function.	—
FunctionWithParams	JavaScript function with parameters.	—
Distribution	Probability distribution.	—
QueuingDiscipline	Ordering rule of a queue.	—
Color	Colour as a hex code #rgb, #rrggbb or #aarrggbb, or a colour name.	"#ff8800", "red"
String	Single-line text.	"Station A"
MultiLineString	Text that may span several lines (use \n for a line break).	"line 1\nline 2"
Symbol	Symbol drawn for an entity, given as a short text such as a letter or emoji.	"A"
AttributeName	Name of an entity attribute, written as a string.	"lastWait"
Code	JavaScript code, written as a string; separate statements with ;.	"M[0]=P[0]*2"

Common Attributes

These attributes are understood by all blocks or by all connectors (Pipe and Wire). They are not repeated in the block sections below.

Attribute	Type	Applies to	Description
label	String	blocks	Caption shown on the block.
active	Bool	blocks	Whether the block takes part in the simulation. It can also be wired into parameterActive, but only on blocks whose Data inputs table lists that connector.
x	FiniteReal	blocks	Horizontal position on the canvas.
y	FiniteReal	blocks	Vertical position on the canvas.
width	PositiveFiniteReal	blocks	Width of the block on the canvas.
height	PositiveFiniteReal	blocks	Height of the block on the canvas.
incline	FiniteReal	Pipe, Wire	Horizontal offset of both control points: how far the line runs out of the source socket before it turns.
decline	FiniteReal	Pipe, Wire	Moves the second control point to the left: how far the line runs into the target socket. Defaults to twice incline.
bend	FiniteReal	Pipe, Wire	Moves the curve vertically: shifts both control points of the line down (negative values: up), so the line bows. Dragging the line in the editor sets it.
debend	FiniteReal	Pipe, Wire	Moves the end of the curve vertically: shifts the second control point up, against bend, so the line enters the target from another angle.
offLayout	Bool	blocks	If true, the block is excluded from the automatic layout.
color	Color	blocks, Pipe, Wire	Colour of the block or of the connector line.
fillColor	Color	blocks	Background colour of the block.
borderColor	Color	blocks	Border colour of the block.
visible	Bool	blocks	Whether the block is drawn.
inPriority	ReadOnly	Pipe, Wire	Set by Quisim: rank among the connectors at the same receiving socket. 1 comes first, and that is the connector defined last in the code.
outPriority	ReadOnly	Pipe, Wire	Set by Quisim: rank among the connectors at the same sending socket. 1 is tried first, and that is the connector defined last in the code.
noPush	Bool	Wire	If true, a value sent through the wire updates the data input quietly: it triggers no computation and counts as no observation, the block uses it when it computes anyway. Breaks feedback loops, or lets only one of several inputs trigger.
noPull	Bool	Wire	If true, a read of the data input is not passed on to the measure, so the measure does not compute a fresh value for it.
noPushRepeats	Bool	Wire	If false (default), every value sent through the wire reaches the data input as a new value, also if it equals the current one, so a Function computes again on two equal results in a row. true passes a value on only if it differs from the current one.

Entity Attributes

Every entity carries attributes. Besides the built-in ones below, you can define your own with a Set block, under any name: priority in the examples of this page is such a self-defined attribute. Entity attributes are read with the Get block. See also Entity Attributes in the handbook.

Attribute	Set by	Meaning
number	Quisim, when the entity is created	Unique number of the entity. Read-only.
color	Arrive or Provide (their color attribute), or a Set block	Colour in which the entity is drawn.
symbol	Arrive or Provide (their symbol attribute), or a Set block	Symbol drawn for the entity.
innerEntitiesLength	Quisim	Number of entities packed into this entity. Read-only.
lastWait	Queue	Time the entity waited in the last queue it left.
lastDuration	Perform	Duration of the last Perform activity.
meterTime	Meter	Simulation time at which the entity passed the last Meter.

lastEntryTime is not an attribute: it is the keyword for arrival order in the criterion of the Queue and Pack blocks, and their default.

While a model runs, the Entities page of Quisim's Monitor tab lists the entities in the selected block together with their attributes.

Connectors

Blocks are connected by two kinds of connectors. Each one names its source with from and its target with to, written as blockId.connector.

Pipe

Transports an entity from an outlet to an inlet.

If several pipes leave the same outlet, an entity is offered to them one after the other, starting with the pipe defined last in the code. Quisim numbers the pipes at a socket in that order as their priority (1 is tried first), see outPriority and inPriority.

Wire

Transmits a value from a measure to a parameter (data input).

If several wires are connected to the same socket, the wire defined last in the code has priority. Quisim numbers the wires at a socket in that order as their priority (1 comes first), see outPriority and inPriority.

Every value sent is passed on, also if it equals the current value of the data input: a Function there computes again, so two equal results in a row stay two. A value sent in answer to a read, or one that returns unchanged around a feedback loop, counts as unchanged. With noPushRepeats, only a changed value is passed on.

With noPush, a value sent updates the data input quietly: it triggers no computation and counts as no observation, the block uses it when it computes anyway (e.g. a Function triggered by another input). Use it to break a feedback loop or to let only one of several inputs trigger. With noPull, a read of the data input is not passed on to the measure, which then does not compute a fresh value.

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Entity Blocks

Entity blocks create, process, route and dispose of entities. Entities travel along pipes between their entity sockets.

Arrive Block

Creates entities at outlet with interarrival times given by duration.

If a created entity is still there when the next entity arrives, the arrival is blocked until the entity is gone, which delays the next interarrival time correspondingly.

When beginAtZero is set, the first entity arrives at time 0, otherwise when passing the first duration. If duration is negative, it is ‘rounded up’ to 0.

Attributes

Attribute	Type	Default	Wire input	Description
duration	NonNegativeReal	1000	parameterDuration	Interarrival time
beginAtZero	Bool	true	—	The first entity arrives at time 0 instead of after the first duration.
color	Color	"#000000"	—	Colour given to each created entity (its color attribute).
symbol	Symbol	""	—	Symbol given to each created entity (its symbol attribute).

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterDuration	D	duration	NonNegativeReal	1000	Interarrival time

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureNumPassed	N	numPassed	Passed entity count	cumulative

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Provide Block

Provisions an entity at outlet at all times, indefinitely. The next entity is created as soon as the previous one is transported away.

Attributes

Attribute	Type	Default	Wire input	Description
color	Color	"#000000"	—	Colour given to each created entity (its color attribute).
symbol	Symbol	""	—	Symbol given to each created entity (its symbol attribute).

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureNumPassed	N	numPassed	Passed entity count	cumulative

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Leave Block

Deletes any entity incoming at the inlet.

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureNumPassed	N	numPassed	Passed entity count	cumulative
measureSojourn	LS	lastSojourn	Sojourn time between entry and leave of last exited entity	—

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Perform Block

Delays any entity incoming at the inlet by the given duration.

If the delayed entity is not transported away instantly from outlet, then the entity takes up capacity as long as it waits.

The activity has a given capacity of entities in parallel, which is unlimited if set to Infinity. Then, multiple entities are processed in parallel, each using the given duration (the value is taken on arrival of the entity), which is ‘rounded up’ to 0 if a negative duration is given.

Two triggers make processed entities excess. With haltExcess, reducing capacity below the number of processed entities makes those ranked last excess; otherwise they keep being processed (soft cap). With preemptive, an arriving entity that strictly outranks the processed entity ranked last is admitted even at full capacity, and that entity becomes excess; an arriving entity that does not outrank it waits upstream.

An excess entity stops being processed. If outEvict (E) can take it right away, it leaves there, with its remaining work in the attribute remainingDuration; otherwise it is halted. A halted entity resumes, best-ranked first, as soon as capacity frees up, or leaves through E as soon as E opens. So piping E decides whether excess entities leave or wait. Finished entities waiting at the outlet are never touched. The load counts halted entities, too.

Entities are ranked according to the criterion in ascending (otherwise descending) order. The default criterion remainingDuration is a keyword for the work still to do (an arriving entity's full duration), so the entity with the most remaining work is ranked last, and preemptive gives shortest remaining processing time first (SRPT). The keyword lastEntryTime is the time of entering the block: not ascending, it gives preemptive LIFO; ascending, it never preempts. Any other criterion names an entity attribute, e.g. a priority.

The E outlet is shown if haltExcess or preemptive is set or it is piped.

Attributes

Attribute	Type	Default	Wire input	Description
capacity	NonNegativeIntWithInfinity	1	parameterCapacity	Parallel processing capacity
duration	NonNegativeReal	1000	parameterDuration	Processing duration
criterion	AttributeName	"remainingDuration"	—	Ranking of the entities in service, the last ranked stops first: the keyword remainingDuration for the work still to do, lastEntryTime for the entry time, otherwise the name of an entity attribute.
ascending	Bool	true	—	Ascending ranking (the entity with most remaining work is ranked last by default); false reverses it.
haltExcess	Bool	false	—	When the capacity drops below the number in service, the entities ranked last stop: they leave through outEvict if it can take them, else they are halted until capacity frees up; false keeps them running (soft cap).
preemptive	Bool	false	—	An arriving entity that strictly outranks the entity in service ranked last takes its place, which leaves through outEvict if it can take it, else is halted.

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet
outEvict	E	Evict

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterCapacity	C	capacity	NonNegativeIntWithInfinity	1	Parallel processing capacity
parameterDuration	D	duration	NonNegativeReal	1000	Processing duration

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureLoad	L	load	Number of held entities: processed, halted, and finished but blocked	time-weighted

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Queue Block

Temporarily stores entities incoming at the inlet.

Outgoing entities are ordered according to the criterion attribute in ascending (otherwise in descending) order. The default criterion lastEntryTime is a keyword, not an entity attribute: it means arrival order, i.e. first in, first out (FIFO), or last in, first out (LIFO) when ascending is false. Any other criterion names an entity attribute (for example one written by a Set block), and entities are sorted by its numeric value.

If the outlet is blocked and renegeDuration is set, the entity is instead sent to outRenege after renegeDuration (the value is taken on arrival of the entity, and ‘rounded up’ to 0 if negative) has passed.

If the capacity is reached, an incoming entity waits upstream, even while the outlet is open. With capacity 0 there is no waiting room: an entity passes straight through only if the next block takes it at once, otherwise it waits upstream. If capacity drops below the load, no entity is removed: the queue just drains (soft cap). With evictExcess, the entity served last is sent to outEvict (E) instead, one at a time until the load fits the capacity. With preemptive, at exactly full capacity, an incoming entity pushes the entity served last out to E if it would be served before it: never with FIFO, always with LIFO, and with any other criterion only if its value is strictly lower (strictly higher if not ascending). While E is blocked or not piped, nothing is removed; removal resumes as soon as E opens.

Outlets are numbered in the order shown: outlet is 0, then R (outRenege) if reneging is enabled – renegeDuration is finite or wired – or R is piped, then E if evictExcess or preemptive is set or E is piped.

Attributes

Attribute	Type	Default	Wire input	Description
capacity	NonNegativeIntWithInfinity	∞	parameterCapacity	Maximum number of enqueued entities
renegeDuration	NonNegativeReal	∞	parameterRenegeDuration	Renege wait duration
criterion	AttributeName	"lastEntryTime"	—	Ordering: the keyword lastEntryTime for arrival order, otherwise the name of an entity attribute to sort by.
ascending	Bool	true	—	Ascending order (FIFO with lastEntryTime); false reverses it (LIFO).
evictExcess	Bool	false	—	On a capacity drop, send the entities served last to outEvict until the load fits the capacity; false keeps a soft cap.
preemptive	Bool	false	—	At full capacity, an arriving entity that would be served earlier pushes the entity served last out to outEvict.

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet
outRenege	R	Renege
outEvict	E	Evict

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterCapacity	C	capacity	NonNegativeIntWithInfinity	∞	Maximum number of enqueued entities
parameterRenegeDuration	RD	renegeDuration	NonNegativeReal	∞	Renege wait duration

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureLoad	L	load	Number of enqueued entities	time-weighted
measureWait	LW	—	Wait time of last exited entity	—

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Meter Block

Counts the number of passed entities from inlet to outlet. If a capacity is set, the meter stops passing entities if reaching this load. The load is reset when the reset parameter is changed to a non-zero value.

The count is the measure measureNumPassed; its name is load, the block property that holds it. Since a reset sets it back to 0, a Statistics block with cumulative set reports the increase since the last reset only if no reset happens during the run.

Attributes

Attribute	Type	Default	Wire input	Description
capacity	NonNegativeIntWithInfinity	∞	parameterCapacity	Maximum number of passed entities
active	Bool	true	parameterActive	Activates this block

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameterCapacity	C	capacity	NonNegativeIntWithInfinity	∞	Maximum number of passed entities
parameterReset	R	— (wire only)	Int	0	Reset when changed to a non-zero value

parameterReset is only driven by a wire; there is no attribute to set as a constant.

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureNumPassed	N	load	Passed entity count since the last reset	cumulative
measureLastInterval	LI	—	Time interval between the two last passing entities	—
measureLastTime	LT	—	Time that the last entity passed	—

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Decide Block

An incoming entity is sent to outlets[S] where S is the selectedOutlet number, which is always rounded down to the next integer. Is blocked if all outlets are blocked.

The default selectedOutlet -1 selects no outlet, and so does any number without a matching outlet: the entity then waits until a valid outlet is selected.

Attributes

Attribute	Type	Default	Wire input	Description
numOutlets	PositiveInt	2	—	Number of outlets.
selectedOutlet	Int	-1	parameterSelectedOutlet	Outlet choice; -1 selects none

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlets[0]	O0	Outlet 0
outlets[1]	O1	Outlet 1

There are numOutlets indexed outlets[…], numbered from 0; the table shows the default numOutlets = 2.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSelectedOutlet	S	selectedOutlet	Int	-1	Outlet choice; -1 selects none

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Select Block

The incoming entity is taken from inlets[S] where S is the selectedInlet number, which is always rounded down to the next integer. An inlet is blocked if it is not selected or the outlet is blocked.

The default selectedInlet -1 selects no inlet, and so does any number without a matching inlet: then all inlets are blocked.

Attributes

Attribute	Type	Default	Wire input	Description
numInlets	PositiveInt	2	—	Number of inlets.
selectedInlet	Int	-1	parameterSelectedInlet	Selected inlet; -1 selects none

Entity inputs (green sockets on the left)

Connector	Label	Description
inlets[0]	I0	Inlet 0
inlets[1]	I1	Inlet 1

There are numInlets indexed inlets[…], numbered from 0; the table shows the default numInlets = 2.

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSelectedInlet	S	selectedInlet	Int	-1	Selected inlet; -1 selects none

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Pack Block

Packs entities incoming at the inlet into the packet entity.

This entity is sent to the outlet as soon as possible, i.e., as soon as the sufficiency number of loaded entities is attained. There are no further entities packed into the container when the capacity limit is reached. By default, sufficiency=capacity, and capacity is ∞.

Inside the packet, the entities are kept in arrival order with the default criterion lastEntryTime, which is a keyword, not an entity attribute. Any other criterion names an entity attribute, and the packed entities are ordered by its numeric value, lowest first.

Attributes

Attribute	Type	Default	Wire input	Description
capacity	NonNegativeIntWithInfinity	∞	parameterCapacity	Upper load that is acceptable
sufficiency	NonNegativeIntWithInfinity	same as capacity	parameterSufficiency	Bottom load for sending a packet
criterion	AttributeName	"lastEntryTime"	—	Order inside the packet: the keyword lastEntryTime for arrival order, otherwise the name of an entity attribute to sort by.

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet
inPacket	P	Packet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterCapacity	C	capacity	NonNegativeIntWithInfinity	∞	Upper load that is acceptable
parameterSufficiency	B	sufficiency	NonNegativeIntWithInfinity	same as capacity	Bottom load for sending a packet

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureLoad	L	load	Load	time-weighted
measureLastLoad	LL	lastLoad	Load of last sent packet entity	—
measureHasPackage	HP	hasPackage	Has a packet for packing?	time-weighted

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Unpack Block

Unpacks a packet entity incoming at the inlet. It is sent to the packet outlet, but only after unpacking, which involves enqueuing and sending at most capacity inner entities to the outlet. See Queue for how to handle the queue, except that its wait measure – relabelled LD here and wired as measureWait – measures elapsed time from the entity's enqueuing in the Pack block until exiting here.

The capacity is read when a packet arrives only. Unpack has no E outlet and ignores criterion: it takes a packet only when empty, and inner entities beyond capacity stay in the packet. R is shown if reneging is enabled or R is piped.

Attributes

Attribute	Type	Default	Wire input	Description
capacity	NonNegativeIntWithInfinity	∞	parameterCapacity	Maximum number of enqueued entities
renegeDuration	NonNegativeReal	∞	parameterRenegeDuration	Renege wait duration
ascending	Bool	true	—	Release the inner entities in packing order; false reverses it.

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet
outRenege	R	Renege
outPack	P	Packet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterCapacity	C	capacity	NonNegativeIntWithInfinity	∞	Maximum number of enqueued entities
parameterRenegeDuration	RD	renegeDuration	NonNegativeReal	∞	Renege wait duration

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureLoad	L	load	Number of enqueued entities	time-weighted
measureWait	LD	—	Pack (containment) duration of last exited entity	—
measureHasPackage	HP	hasPackage	Has a package for unpacking?	time-weighted

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Set Block

Sets the attribute named attribute of a passing entity to value. You may choose the attribute name freely, but the read-only attributes number and innerEntitiesLength cannot be set.

See Entity Attributes for the attributes every entity has.

Attributes

Attribute	Type	Default	Wire input	Description
attribute	AttributeName	""	parameterAttribute	Attribute name
value	Variant	—	parameterValue	Attribute value

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterAttribute	A	attribute	AttributeName	""	Attribute name
parameterValue	V	value	Variant	—	Attribute value

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Get Block

Gets the value of attribute named attribute of an entity that is being evaluated for being passed through.

See Entity Attributes for the attributes every entity has.

Attributes

Attribute	Type	Default	Wire input	Description
attribute	AttributeName	""	parameterAttribute	Attribute name

Entity inputs (green sockets on the left)

Connector	Label	Description
inlet	I	Inlet

Entity outputs (green sockets on the right)

Connector	Label	Description
outlet	O	Outlet

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterAttribute	A	attribute	AttributeName	""	Attribute name

Measures (silver sockets on the right)

Connector	Label	Description	Statistics
measureValue	V	Attribute value	—

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Lineup Block

Synchronizes multiple entities. There are numConnectors inlets and outlets. Transports an entity from inlets[i] to outlets[i] for each 0≤i<numConnectors whereby the block waits until neededEntities have arrived at distinct inlets (so the entities line up), then waits again until all entities can be sent out at once, each at its corresponding outlet. There must be 1≤neededEntities≤numConnectors. By default, neededEntities=numConnectors.

Attributes

Attribute	Type	Default	Wire input	Description
numConnectors	PositiveInt	2	—	Number of inlet/outlet pairs.
neededEntities	PositiveInt	same as numConnectors	parameterNeededEntities	Number of needed entities per gathering

Entity inputs (green sockets on the left)

Connector	Label	Description
inlets[0]	I0	Inlet 0
inlets[1]	I1	Inlet 1

There are numConnectors indexed inlets[…], numbered from 0; the table shows the default numConnectors = 2.

Entity outputs (green sockets on the right)

Connector	Label	Description
outlets[0]	O0	Outlet 0
outlets[1]	O1	Outlet 1

There are numConnectors indexed outlets[…], numbered from 0; the table shows the default numConnectors = 2.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterNeededEntities	C	neededEntities	PositiveInt	same as numConnectors	Number of needed entities per gathering

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureNumAvailable	L	numAvailable	Available entity count	time-weighted

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Value Blocks

Value blocks set values, compute functions and run experiments. They have data sockets only.

Explorer Block

Explores simulation runs in a loop by configuring and evaluating them. Use the parameter array P and set the measure array M. The yield command starts the next run and returns when finished. The return value is true unless the last experiment is done.

Attributes

Attribute	Type	Default	Wire input	Description
code	Code	"while(true)\n{ yield }"	—	JavaScript code of the exploration loop: reads P, sets M, and calls yield to start the next run.
numParameters	PositiveInt	1	—	Number of data inputs parameters[0], parameters[1], ….
numMeasures	PositiveInt	1	—	Number of measures measures[0], measures[1], ….
plotLogScaled	Bool	false	—	Logarithmic plot scale (currently has no effect).

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameters[0]	P[0]	—	Variant	—	Parameter 0

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

Measures (silver sockets on the right)

Connector	Label	Description	Statistics
measures[0]	M[0]	Measure 0	—

There are numMeasures indexed measures[…], numbered from 0; the table shows the default numMeasures = 1.

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Note Block

Displays a text note.

Attributes

Attribute	Type	Default	Wire input	Description
text	MultiLineString	""	—	Text of the note.
fontBold	Bool	false	—	Bold text.
fontSize	PositiveInt	11	—	Font size in pixels.
fontColor	Color	"#000000"	—	Text colour.

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Number Block

Shows a number, either fixed or from the inlet, and provides it at the output.

Attributes

Attribute	Type	Default	Wire input	Description
value	Real	0	parameterValue	Number value

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterValue	V	value	Real	0	Number value

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureValue	N	value	Number value	—

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NumberInput Block

Editable number that is provided at the output.

Attributes

Attribute	Type	Default	Wire input	Description
initValue	Real	1	—	Value when the model is loaded; it can then be edited while the simulation runs.
lower	Real	−∞	—	Smallest value that can be entered.
upper	Real	∞	—	Largest value that can be entered.

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureValue	N	value	Number value	—

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Function Block

Calls the stated function code, reading the parameter array P and writing to the measure array M.

The context code is evaluated once on model load, global functions and variables may be created here. The init code is evaluated on each simulation run start to reset global variables.

On parameter update and on measure read request, an immediate function evaluation happens unless deferredEvaluation is set, which schedules an update event. However, numRequested and numConsumed parameters do not trigger evaluation (and the latter does not issue read requests itself). The default M[0]=P[0] code copies the first parameter to the first measure.

Array values are accessed using P[i] and M[j] where 0≤i<numParameters+numRequested+numConsumed and 0≤j<numMeasures; both arrays start at index 0. A reusable context code can be given.

Multiple statements must be separated by ; (semicolon).

The JavaScript Math functions can be used (Math.max(), Math.min(), etc.).

Example function text that both adds and subtracts the two parameters and yields two measures: M[0]=P[0]+P[1];M[1]=P[0]-P[1]

Attributes

Attribute	Type	Default	Wire input	Description
context	Code	""	—	JavaScript code evaluated once on model load, for global functions and variables.
init	Code	""	—	JavaScript code evaluated at the start of each run, to reset global variables.
code	Code	"M[0]=P[0]"	—	JavaScript code that reads P and writes M.
numParameters	NonNegativeInt	1	—	Number of parameters that trigger an evaluation when they change.
numRequested	NonNegativeInt	0	—	Number of further parameters that are read on evaluation but do not trigger it.
numConsumed	NonNegativeInt	0	—	Number of further parameters that neither trigger an evaluation nor request a value themselves.
numMeasures	NonNegativeInt	1	—	Number of measures measures[0], measures[1], ….
active	Bool	true	parameterActive	Activates this block
deferredEvaluation	Bool	false	—	Evaluate in a scheduled update event instead of immediately.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameters[0]	P[0]	—	Variant	—	parameter triggers read on recompute, and itself triggers recompute if it changes

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

Measures (silver sockets on the right)

Connector	Label	Description	Statistics
measures[0]	M[0]	Measure 0	—

There are numMeasures indexed measures[…], numbered from 0; the table shows the default numMeasures = 1.

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Aggregate Block

Reduces incoming value to an aggregate value, initialized with init. For example, a sum is attained with A+=V and init 0. The aggregate is reset to init when the reset parameter is changed to a non-zero value.

Attributes

Attribute	Type	Default	Wire input	Description
code	Code	"A=V??A"	—	JavaScript code that combines the aggregate A with the incoming value V, for example A+=V.
init	Variant	—	parameterInit	Initial value for A
value	Variant	—	parameterValue	Input value V, change triggers computation event
ignoreNaN	Bool	true	—	Skip incoming NaN (not a number) values.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameterValue	V	value	Variant	—	Input value V, change triggers computation event
parameterInit	I	init	Variant	—	Initial value for A
parameterReset	R	— (wire only)	Int	0	Reset when changed to a non-zero value

parameterReset is only driven by a wire; there is no attribute to set as a constant.

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureAggregate	A	aggregate	Aggregate value A	—

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BrentOptimize Block

The Brent algorithm optimizes the given objective locally in the range [lower,upper]. The search sets the first guess to init if given. The current guess is adapted when a new run is started.

Attributes

Attribute	Type	Default	Wire input	Description
minimize	Bool	true	—	Minimize the objective; false maximizes it.
objective	Real	0	parameterObjective	Objective value
lower	Real	−∞	parameterLower	Lower bound of the search interval
upper	Real	∞	parameterUpper	Upper bound of the search interval
init	Real	lower + 0.382·(upper − lower), the golden-section point of the search interval (0 if the interval is unbounded)	parameterInit	Initial parameter
plotLogScaled	Bool	false	—	Plot the objective history on a logarithmic scale.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterObjective	V	objective	Real	0	Objective value
parameterLower	min	lower	Real	−∞	Lower bound of the search interval
parameterUpper	max	upper	Real	∞	Upper bound of the search interval
parameterInit	I	init	Real	lower + 0.382·(upper − lower), the golden-section point of the search interval (0 if the interval is unbounded)	Initial parameter

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureGuess	G	guess	Parameter guess	—
measureBest	B	best	Best guess found	—

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Random Number Blocks

Random number blocks are value blocks that draw random variates from a probability distribution, for example for inter-arrival or service times. Each has its own seed, so runs are reproducible.

RandomUniform Block

Generates uniformly distributed random variates in the range [lower,upper).

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
lower	FiniteReal	0	parameterLower	Lower value
upper	FiniteReal	1	parameterUpper	Upper value
floor	Bool	false	—	Round each variate down to a whole number.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterLower	min	lower	FiniteReal	0	Lower value
parameterUpper	max	upper	FiniteReal	1	Upper value

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomExponential Block

Generates exponentially distributed random variates adhering to the given mean µ, which is the inverse of the rate λ.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
mean	NonNegativeReal	1	parameterMean	Mean µ=1/λ
lower	NonNegativeReal	0	parameterLower	Lower value of the truncated distribution
upper	NonNegativeReal	∞	parameterUpper	Upper value of the truncated distribution
floor	Bool	false	—	Round each variate down to a whole number.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterMean	µ	mean	NonNegativeReal	1	Mean µ=1/λ
parameterLower	min	lower	NonNegativeReal	0	Lower value of the truncated distribution
parameterUpper	max	upper	NonNegativeReal	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomPoisson Block

Generates Poisson distributed random variates adhering to the given mean µ.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
mean	NonNegativeReal	1	parameterMean	Mean µ
lower	Real	−∞	parameterLower	Lower value of the truncated distribution
upper	NonNegativeReal	∞	parameterUpper	Upper value of the truncated distribution

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterMean	µ	mean	NonNegativeReal	1	Mean µ
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	NonNegativeReal	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomDiscrete Block

Generates discrete random variates: outcome i is drawn with probability probabilities[i] and yields values[i], or i itself if values is empty, i.e., 0,1,…,n-1 for n probabilities.

The probabilities are non-negative weights normalized by their sum. A non-empty values list must have the same length.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
probabilities	Array	"0.5,0.5"	—	Non-negative weights of the outcomes, normalized by their sum, e.g. [0.2, 0.5, 0.3].
values	Array	0, 1, …, n−1 for n probabilities	—	Value of each outcome, same length as probabilities; empty means 0, 1, …, n−1.
labels	Array	""	—	Optional name of each outcome, e.g. ["walk-in", "appointment"]; display only, a length mismatch is ignored.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomContinuous Block

Generates continuous random variates from an empirical distribution by the inversion method: the cumulative distribution function F is linear between support points, and each variate is F⁻¹(u) for a uniform u.

Without frequencies, values are raw measurements in any order: every gap between the sorted values gets the same probability (Law & Kelton), so tied values form a point mass. To let variates fall outside the observed range (Banks et al.), add the extra support point to values, e.g. 0 for durations.

With frequencies, a histogram is given: values are its k+1 non-decreasing bin edges and frequencies the k relative frequencies of the bins, normalized by their sum; variates are uniform within each bin, and a bin of zero width is a point mass.

In both cases lower/upper truncate the distribution like for the other random blocks: a bin or gap cut by a bound keeps the share of its probability that lies inside, and a point mass is kept if it lies within the bounds.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
values	Array	"0,1"	—	Without frequencies: raw measurements in any order, each gap between the sorted values gets equal probability, e.g. [3.1, 4.7, 5.2, 8.0]; add an extra support point such as 0 to extend the range (Banks). With frequencies: the k+1 histogram bin edges, non-decreasing, a zero-width bin is a point mass.
frequencies	Array	""	—	Optional; non-empty selects histogram mode: non-negative relative frequency of each bin, one fewer than values, normalized by their sum.
lower	Real	−∞	parameterLower	Lower value of the truncated distribution, in both modes.
upper	Real	∞	parameterUpper	Upper value of the truncated distribution, in both modes.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	Real	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomPiecewiseLinear Block

Generates continuous random variates whose density is piecewise linear: values are k+1 non-decreasing points and densities the density at each point, linear in between and normalized by the total area. A repeated value is a jump of the density, e.g. values [0,1,1,2] with densities [1,1,3,3] is a step. The triangular distribution with lower, mode, upper is values [lower,mode,upper] with densities [0,1,0].

lower/upper truncate the distribution like for the other random blocks: the density is cut at the bounds.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
values	Array	"0,1"	—	k+1 non-decreasing points, at least 2, e.g. [0, 1, 3, 4]; a repeated value is a jump of the density there.
densities	Array	"1,1"	—	Non-negative density at each point, same length as values, linear in between and normalized by the total area, e.g. [0, 2, 1, 0]; at a jump the first and last density of the repeated value are the left and right limits.
lower	Real	−∞	parameterLower	Lower value of the truncated distribution: the density is cut there.
upper	Real	∞	parameterUpper	Upper value of the truncated distribution: the density is cut there.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	Real	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomGamma Block

Generates gamma distributed random variates adhering to the given shape k and scale θ.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
shape	NonNegativeReal	1	parameterShape	Shape k of gamma distribution
scale	NonNegativeReal	1	parameterScale	Scale θ of gamma distribution
lower	Real	−∞	parameterLower	Lower value of the truncated distribution
upper	NonNegativeReal	∞	parameterUpper	Upper value of the truncated distribution

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterShape	k	shape	NonNegativeReal	1	Shape k of gamma distribution
parameterScale	θ	scale	NonNegativeReal	1	Scale θ of gamma distribution
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	NonNegativeReal	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomWeibull Block

Generates Weibull distributed random variates adhering to the given shape k and scale θ.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
shape	NonNegativeReal	1	parameterShape	Shape k of Weibull distribution
scale	NonNegativeReal	1	parameterScale	Scale θ of Weibull distribution
lower	Real	−∞	parameterLower	Lower value of the truncated distribution
upper	NonNegativeReal	∞	parameterUpper	Upper value of the truncated distribution

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterShape	k	shape	NonNegativeReal	1	Shape k of Weibull distribution
parameterScale	θ	scale	NonNegativeReal	1	Scale θ of Weibull distribution
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	NonNegativeReal	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomNormal Block

Generates normally distributed random variates adhering to the given mean µ and standard deviation stddev σ.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
mean	Real	0	parameterMean	Mean µ (location) of normal distribution
stddev	NonNegativeReal	1	parameterStddev	Standard deviation σ (shape) of normal distribution
lower	Real	−∞	parameterLower	Lower value of the truncated distribution
upper	Real	∞	parameterUpper	Upper value of the truncated distribution

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterMean	µ	mean	Real	0	Mean µ (location) of normal distribution
parameterStddev	σ	stddev	NonNegativeReal	1	Standard deviation σ (shape) of normal distribution
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	Real	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomLognormal Block

Generates log-normally distributed random variates adhering to the given mean µ and standard deviation stddev σ.

Here mean is the location parameter µ of the underlying normal, not the average of the variates: the median is exp(mean) and the arithmetic mean is larger still. To target an average value, do not set mean to it directly.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
mean	Real	1	parameterMean	Location µ (exp(mean)) of lognormal distribution
stddev	NonNegativeReal	1	parameterStddev	Standard deviation σ (shape) of lognormal distribution
lower	Real	−∞	parameterLower	Lower value of the truncated distribution
upper	NonNegativeReal	∞	parameterUpper	Upper value of the truncated distribution

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterMean	µ	mean	Real	1	Location µ (exp(mean)) of lognormal distribution
parameterStddev	σ	stddev	NonNegativeReal	1	Standard deviation σ (shape) of lognormal distribution
parameterLower	min	lower	Real	−∞	Lower value of the truncated distribution
parameterUpper	max	upper	NonNegativeReal	∞	Upper value of the truncated distribution

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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RandomPERT Block

Generates PERT distributed random variates in the range [lower,upper] for an expert estimate: at least lower, most likely mode, at most upper, where lower≤mode≤upper. It is a beta distribution rescaled to the range with mean (lower+shape·mode+upper)/(shape+2).

The shape λ is the weight of the mode: 4 is the classic PERT, larger values concentrate the variates more around the mode, and 0 is the uniform distribution.

Attributes

Attribute	Type	Default	Wire input	Description
seed	NonNegativeInt	0	parameterSeed	Seed value for the random number generator. If set to 0, the global generator is used.
lower	Real	0	parameterLower	Lower value: at least
mode	Real	(lower + upper)/2	parameterMode	Mode value: most likely
upper	Real	1	parameterUpper	Upper value: at most
shape	NonNegativeReal	4	parameterShape	Shape λ: weight of the mode, 4 is the classic PERT, 0 is uniform

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterSeed	S	seed	NonNegativeInt	0	Seed value for the random number generator. If set to 0, the global generator is used.
parameterLower	lower	lower	Real	0	Lower value: at least
parameterMode	mode	mode	Real	(lower + upper)/2	Mode value: most likely
parameterUpper	upper	upper	Real	1	Upper value: at most
parameterShape	λ	shape	NonNegativeReal	4	Shape λ: weight of the mode, 4 is the classic PERT, 0 is uniform

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureRandomVariate	V	nextVariate	Random variate	—
measureExpectedValue	EV	expectedValue	Expected value for the given parameterization, including the limits	—
measureExpectedStdDev	ESD	expectedStdDev	Expected standard deviation for the given parameterization, including the limits	—

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Report Blocks

Report blocks display simulation data and compute statistics from the values wired into their parameter array parameters[0], parameters[1], ….

Chart Block

Shows a stairstep line chart of the given input parameters P. Each input is drawn in another color.

With multiple replications or editions, the x-axis lays them out side-by-side by default. Set overlayReplications to superimpose all replications within an edition into one window, and overlayEditions to superimpose editions. Setting both yields a spaghetti plot where every trajectory overlaps in a single window. Set clearBetweenReplications or clearBetweenEditions to drop the earlier runs' series whenever a new replication or edition starts.

By default, the x-axis is simulation time. Unset timeAbscissa to switch to a running observation count instead: each appended sample advances x by one, regardless of the simulation time at which it arrived. Useful when interesting structure is compressed by sporadic or bursty event timing — e.g. a queue length that updates rarely between long idle periods. Slot widths are sized by the longest leaf's observation count, leaving blank tails for shorter ones; raw (time, value) data is retained so toggling is lossless.

Attributes

Attribute	Type	Default	Wire input	Description
numParameters	PositiveInt	1	—	Number of data inputs parameters[0], parameters[1], ….
overlayReplications	Bool	false	—	Draw all replications of an edition on top of each other.
overlayEditions	Bool	false	—	Draw all editions on top of each other.
clearBetweenReplications	Bool	false	—	Drop the earlier data when a new replication starts.
clearBetweenEditions	Bool	false	—	Drop the earlier data when a new edition starts.
timeAbscissa	Bool	true	—	The x-axis is simulation time; false uses a running observation count.
logScale	Bool	false	—	Logarithmic y-axis.
sameScale	Bool	false	—	All inputs share one y range.
active	Bool	true	parameterActive	Activates this block

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameters[0]	P[0]	—	Variant	—	Parameter 0

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

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Histogram Block

Displays a histogram of the given input parameters P. Each input is drawn in another color.

Attributes

Attribute	Type	Default	Wire input	Description
numParameters	PositiveInt	1	—	Number of data inputs parameters[0], parameters[1], ….
active	Bool	true	parameterActive	Activates this block
resetOnRunStart	Bool	false	—	Start a new histogram with every run; false collects all runs into one histogram.
timeAverage	Bool	false	—	Weight each value by how long it held, for levels such as a queue length.
numBuckets	NonNegativeInt	0	—	Number of buckets; 0 chooses it automatically.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameters[0]	P[0]	—	Variant	—	Parameter 0

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

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Report Block

Collects a protocol of the given input parameters P.

Attributes

Attribute	Type	Default	Wire input	Description
numParameters	PositiveInt	1	—	Number of data inputs parameters[0], parameters[1], ….
active	Bool	true	parameterActive	Activates this block
clearBetweenReplications	Bool	true	—	Drop the earlier data when a new replication starts.
clearBetweenEditions	Bool	true	—	Drop the earlier data when a new edition starts.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameters[0]	P[0]	—	Variant	—	Parameter 0

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

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Statistics Block

Computes mean values and confidence intervals of the given input parameters P.

The mean is an arithmetic mean – unless timeAverage is set: then it is time-weighted, which suits levels that vary over time, such as a queue length.

If multiple runs or batches are available, confidence levels are calculated using the Student t distribution, with the radius by the confidence attribute. The confidence must be in the range (0,1). The confidence attribute is the tail probability α, not the coverage: set it to 0.05 for a 95% interval or 0.01 for a 99% interval, since the reported interval is 1−confidence. If only a single run or batch is available, no confidence intervals are calculated.

The output measures are laid out in two contiguous blocks, not interleaved: for numParameters inputs, measures[0]…measures[numParameters−1] are the per-input means labelled M[0]…M[numParameters−1], and measures[numParameters]…measures[2·numParameters−1] are the means of means labelled MM[0]…MM[numParameters−1]. So the mean of the second input is measures[1] (M[1]) while its mean of means is measures[numParameters+1] (MM[1]).

Advanced options. Instead of a mean, setting cumulative yields the difference between start and end values.

Setting quantile to a value in [0,1] reports a per-batch quantile instead of a mean (0 = min, 1 = max); cross-batch aggregation and the confidence interval are computed the same way. With timeAverage the per-batch quantile is time-weighted by segment duration; otherwise Type-7 linear interpolation between order statistics is used. quantile overrides cumulative.

The timeWidths flag controls how warmupWidth and batchWidth are interpreted: when true (default), both are simulation-time durations; when false, both are record counts. Observations before the warmup are not considered in the statistics — in time mode that means records with time < warmupWidth; in count mode it means the first warmupWidth records of each replication.

When warmupMSER is set and warmupWidth is 0, the warmup is chosen automatically by the MSER-5 rule (White 1997; Hoad/Robinson/Davies 2010), applied to ensemble-averaged chunks of 5 raw observations per parameter. The maximum suggestion across parameters is written into warmupWidth (a simulation time in time mode, a record count in count mode), triggering a recalculation. That attribute can also be flipped on after the simulation has run: as long as warmupWidth is 0, MSER will be evaluated and applied retroactively. Switching it off again resets warmupWidth to 0, unless you have changed warmupWidth yourself in the meantime.

Note: when warmupMSER picks the warmup from the same data that the statistics are computed on, the resulting confidence interval is mildly anti-conservative (slightly too narrow), because the same observations are used to both choose the truncation and estimate the mean. For tight-precision claims, use a pilot edition to choose the warmup and apply it to a fresh production edition.

The batch means procedure is configured using batchWidth and numBatches. In time-mode batchWidth denotes the Δt per batch; in count-mode batchWidth denotes the number of consecutive records per batch and is resolved post-hoc — leave it NaN to derive from numBatches, or set it explicitly (which then takes precedence over numBatches).

Setting ar1Correction widens the confidence interval when consecutive batch means are positively correlated: the variance of the mean is multiplied by (1+ρ)/(1−ρ), where ρ is the lag-1 autocorrelation of the batch means. Use it when the batches are short compared with how long the data stay correlated.

A recalculation after the run (a changed setting, or warmupMSER writing its warmup) only updates this block's own view: the wired blocks keep what was sent during the run; run again to pass the new results on.

Attributes

Attribute	Type	Default	Wire input	Description
numParameters	PositiveInt	1	—	Number of data inputs parameters[0], parameters[1], ….
active	Bool	true	parameterActive	Activates this block
confidence	Real01	0.05	—	Tail probability α of the confidence interval: 0.05 gives a 95% interval.
quantile	Real01Closed	NaN	—	Report this per-batch quantile (0 = min, 1 = max) instead of a mean; NaN reports the mean.
timeAverage	Bool	false	—	Weight each value by how long it held, for levels such as a queue length.
cumulative	Bool	false	—	Report the increase from start to end instead of a mean.
warmupWidth	NonNegativeFiniteReal	0	—	Warm-up to discard: a simulation time, or a record count when timeWidths is false.
warmupMSER	Bool	false	—	Choose the warm-up automatically by the MSER-5 rule while warmupWidth is 0.
numBatches	PositiveInt	1	—	Number of batches per run for the batch means method.
batchWidth	NonNegativeReal	(duration − warmupWidth)/numBatches, or NaN when timeWidths is false	—	Width of a batch: a simulation-time span, or a record count when timeWidths is false; NaN derives it from numBatches.
timeWidths	Bool	true	—	warmupWidth and batchWidth are simulation times; false makes them record counts.
ar1Correction	Bool	false	—	Widen the confidence interval for positively correlated batch means.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameters[0]	P[0]	—	Variant	—	Parameter 0

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

Measures (silver sockets on the right)

Connector	Label	Description	Statistics
measures[0]	M[0]	Last mean value	—
measures[1]	MM[0]	Last mean of means	—

There are 2·numParameters (the means M[…] first, then the means of means MM[…]) indexed measures[…], numbered from 0; the table shows the default numParameters = 1.

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SummaryStatistics Block

Estimates the mean and confidence interval of a sample of results, such as the batch means M of a Statistics block wired into its input V.

Every incoming value is one observation: there is no time weighting, warmup or batching. Values are grouped by edition and replication as they arrive, kept in their order within a replication; NaN values are ignored. At the end of each edition, the mean, confidence interval and standard deviation are computed exactly as Statistics computes its mean of means MM from its batch means: per replication the mean of its values, over the replications the Student t interval with R−1 degrees of freedom, or with a single replication with n−1 from its values. So Statistics.M wired in reproduces MM and its interval, and a Function in between (e.g. M[0]=P[0]<=20?1:0) gives the estimate of a derived result.

confidence is the tail probability α (0.05 gives a 95% interval). ar1Correction widens the interval when consecutive values within a replication are positively correlated, as in Statistics. Setting quantile to a value in [0,1] (0 = min, 1 = max) adds the measure Q: the sample quantile per edition over the same unit as the interval, the replication means (the values with a single replication), without a confidence interval.

The measures are the mean, the half-width ± and the limits L and U of the confidence interval, the standard deviation SD of the replication means (of the values with a single replication), the number N of values used and, with quantile set, the quantile Q. Values arriving outside a replication, such as the MM of a Statistics, are ignored. The results of an edition are set at its end together with the MM of the Statistics blocks, before edition-end blocks such as the optimizers read their inputs.

What flowed during the run is what the wired blocks received: a recalculation after the run only updates the view of the recalculating block, such as this one after confidence, ar1Correction or quantile changed, or a Statistics after a setting changed or after warmupMSER chose its warmup at the end of the run (this block then keeps the run's estimate). Run again to pass the new results on (MSER's warmup then holds from the start).

Attributes

Attribute	Type	Default	Wire input	Description
active	Bool	true	parameterActive	Activates this block
confidence	Real01	0.05	—	Tail probability α of the confidence interval: 0.05 gives a 95% interval.
quantile	Real01Closed	NaN	—	NaN (default): no quantile; otherwise the measure Q is this quantile in [0,1] (0 = min, 1 = max) of the replication means (of the values with a single replication), per edition, without a confidence interval.
ar1Correction	Bool	false	—	Widen the interval by (1+ρ)/(1−ρ) for a positive lag-1 autocorrelation ρ of the values within a replication.

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameterValue	V	—	Variant	—	Result value, one observation each (e.g. Statistics M)

Measures (silver sockets on the right)

Connector	Label	Name	Description	Statistics
measureMean	M	mean	Mean of the last edition	—
measureHalfWidth	±	halfWidth	Confidence interval half-width of the mean	—
measureLower	L	lower	Lower confidence limit of the mean	—
measureUpper	U	upper	Upper confidence limit of the mean	—
measureSd	SD	sd	Standard deviation of the replication means (of the values with one replication)	—
measureN	N	n	Number of values used	—
measureQuantile	Q	quantile	Quantile of the replication means (of the values with one replication), with quantile set	—

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EnsemblePlot Block

Displays a Welch plot of the given input parameters P, used to identify the warm-up (transient) phase before steady state. Needs multiple replications to be meaningful; about 5 or more is typical.

Across replications, each input is ensemble-averaged by observation index — Ȳⱼ = (1/R) Σᵢ Yᵢⱼ — then smoothed with a moving average of half-width windowWidth (window size 2w+1, shrinking near the edges; windowWidth = 0 disables smoothing and plots the raw ensemble mean). The warm-up duration is read off where the smoothed curve flattens. With the default batchWidth = 1 this is Welch's (1983) canonical procedure: one plot point per observation index. Setting batchWidth = N > 1 first averages N consecutive observations into each plot point (batch means; Law, Simulation Modeling & Analysis), a standard refinement for legibility on long runs that preserves the same warm-up reading. Unlike the Statistics block (which defaults to time-defined batching in both modes via timeWidths for cross-parameter comparability), EnsemblePlot batches by sample count in obs mode — matching Welch's obs-indexed definition and keeping batch variance homogeneous through the early ramp-up region where the warm-up diagnostic matters most. As a rule of thumb, keep windowWidth below ¼ of the resulting plot's bucket count to avoid over-smoothing.

When timeAverage is set, buckets are time-weighted across the simulation duration (suitable for continuous-time state variables like queue length); batchWidth then denotes the Δt per bucket and auto-defaults to duration/100. Otherwise, the canonical observation-indexed Welch procedure is used, bucketing each replication's observations into non-overlapping batches of size batchWidth (suitable for per-event observations like waiting times). By default ragged is set, so bucketing extends to ⌊max(mᵢ)/N⌋ batches and each bucket is averaged only over the replications that reach it (tail buckets noisier but no data discarded). With timeAbscissa, the per-bucket mean time is clamped to monotonic at rep-dropout boundaries — visible as small plateaus marking where the contributing-rep count drops. Turn ragged off for canonical Welch with uniform R per bucket (cap at ⌊min(mᵢ)/N⌋, longer reps' tails dropped).

When timeAbscissa is set (the default), each obs-indexed batch is positioned on the time axis at the mean simulation time of its constituent observations (averaged across replications), so the x-axis is directly comparable with other time-indexed plots; otherwise batches are placed at evenly-spaced positions across [0, duration]. timeAbscissa has no effect when timeAverage is set, since time-averaged buckets are already time-defined.

When showMser is set, a dashed vertical line per parameter marks the truncation point recommended by MSER-5 (White 1997; Hoad/Robinson/Davies 2010), computed on the ensemble bucket means — useful for confirming the visually-identified warmup. For an automated warmup applied to downstream statistics, set the Statistics block's warmupMSER attribute instead, which evaluates MSER-5 on raw observations and writes the suggestion into warmupWidth. That attribute can also be flipped on after the simulation has run: as long as warmupWidth is 0, MSER will be evaluated and applied retroactively.

When showRaw is set, the un-smoothed ensemble mean is drawn as a faint underlay behind the smoothed line, revealing what the moving average is removing.

All knobs (batchWidth, ragged, windowWidth, timeAverage, timeAbscissa) can be changed after the simulation has finished and the plot re-derives from retained raw data.

Attributes

Attribute	Type	Default	Wire input	Description
numParameters	PositiveInt	1	—	Number of data inputs parameters[0], parameters[1], ….
batchWidth	PositiveFiniteReal	1, or duration/100 when timeAverage is set	—	Observations per plot point, or the time span per bucket when timeAverage is set.
ragged	Bool	true	—	Average each bucket only over the replications that reach it; false cuts all replications to the shortest one.
windowWidth	NonNegativeInt	5	—	Half-width w of the moving average (window 2w+1); 0 disables smoothing.
timeAverage	Bool	false	—	Weight each value by how long it held, for levels such as a queue length.
timeAbscissa	Bool	true	—	Place each point at the mean simulation time of its observations; false spaces the points evenly.
showMser	Bool	true	—	Mark the MSER-5 truncation point with a dashed line.
showRaw	Bool	false	—	Draw the unsmoothed ensemble mean underneath.
logScale	Bool	false	—	Logarithmic y-axis.
sameScale	Bool	false	—	All inputs share one y range; false gives each its own.
active	Bool	true	parameterActive	Activates this block

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameters[0]	P[0]	—	Variant	—	Parameter 0

There are numParameters indexed parameters[…], numbered from 0; the table shows the default numParameters = 1.

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Steering

Every model has one Steering block. It holds the run settings: how long the simulation runs, how many replications and editions it makes, the global seed and the animation speed.

Steering Block

Settings for the simulation like duration, number of replications, global seed, and simulation speed. Display of current virtual time and the current replication number. When input active is set to false, the simulation is halted.

Attributes

Attribute	Type	Default	Wire input	Description
duration	NonNegativeReal	10000	parameterDuration	Duration
editions	PositiveInt	1	parameterEditions	Editions
replications	PositiveInt	1	parameterReplications	Replications
seed	PositiveInt	time-based, so every start differs	parameterSeed	Global seed
speed	PositiveReal	1	parameterSpeed	Speed (velocity) of animation
active	Bool	true	parameterActive	Activates this block

Data inputs (silver sockets on the left)

Connector	Label	Attribute	Type	Default	Description
parameterActive	—	active	Bool	true	Activates this block
parameterDuration	D	duration	NonNegativeReal	10000	Duration
parameterEditions	E	editions	PositiveInt	1	Editions
parameterReplications	R	replications	PositiveInt	1	Replications
parameterSeed	S	seed	PositiveInt	time-based, so every start differs	Global seed
parameterSpeed	V	speed	PositiveReal	1	Speed (velocity) of animation

Measures (silver sockets on the right)

Connector	Label	Description	Statistics
measureCurrentVirtualTime	T	Current virtual time	—
measureFinished	F	Current simulation run finished?	—
measureEdition	EN	Current edition number	—
measureReplication	RN	Current replication number	—

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Glossary

Attribute: A property of a block that you set inside its braces, such as capacity: 5.

Batch means: A way to get a confidence interval from one long run: the run is cut into batches, and the batch averages are treated as independent observations.

Block: A building block of a model, such as Arrive, Queue or Statistics. Each has an id.

Confidence interval: A range around an estimated mean that contains the true mean with a chosen probability, such as 95%.

Connector: A Pipe or Wire that links two sockets. It is written with from and to.

Edition: One configuration of the model. Several editions run the model with different settings, for example to compare designs or to optimize.

Ensemble: All replications of an edition taken together. An ensemble average averages them at the same observation index or time.

Entity: An object that moves through the entity blocks, such as a customer or a part.

Entity attribute: A named value carried by an entity, such as lastWait. See Entity Attributes.

Measure: A value that a block reports at a data socket on its right, such as a queue length.

MSER-5: A rule that chooses the warm-up length automatically: it cuts off the start of the run where that makes the remaining data most stable, working on averages of 5 observations.

Parameter: A data input of a block, on its left. A Wire can drive it during the run.

Renege: To give up waiting: a queued entity leaves through outRenege when its renege duration has passed.

Replication: A repeated run of the same edition with different random numbers. Results are averaged over the replications.

Seed: The start value of a random number generator. The same seed gives the same random numbers, so runs can be repeated.

Socket: A connection point of a block: green entity sockets for pipes, silver data sockets for wires.

Sojourn time: The total time an entity spends in the model, from its creation until it leaves.

Time-weighted: Averaged over time: each value counts as long as it held. Right for levels such as a queue length.

Variate: One random number drawn from a probability distribution.

Warm-up: The start phase of a run, before the model settles into a steady state. It is usually left out of the statistics.

Welch plot: A plot of the ensemble average over time, smoothed with a moving average. The point where it flattens shows the end of the warm-up. See the EnsemblePlot block.

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