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English term or phrase: Provides items for a discrete event simulation at specified interarrival times.  Provides items for a discrete event simulation at specified interarrival times. Choose either a distribution on the left, or choose the empirical distribution and enter probabilities in the table. Items can be created with a random distribution or at a constant rate of arrival. You can also specify the number of items output at each event in the dialog or at the V connector.
This block provides items at specified interarrival rates. Since it always pushes items, this block should usually be followed by a Queue or Resource block when used to provide items for the model. Otherwise, you may lose some items that are generated. If an arrival rate of 0 or less occurs, items are generated immediately (at the time the 0 or less value occurs).
The parameters for the distribution arrival times are set in the dialog. The random distributions include: beta, binomial, constant, empirical, Erlang, exponential, gamma, hyperexponential, log normal, normal, Pearson type V, Pearson type VI, Poisson, Triangular, uniform integer, uniform real, and Weibull. The empirical distribution may have up to 20 points and may be interpreted as a discrete, stepped, or interpolated distribution. The input connectors 1, 2, and 3 allow you to change the parameters of the random distribution as the simulation progresses.
Dialog Choices
(1): A variable that changes with the type of random number.
(2): A variable that changes with the type of random number.
(3): A variable that changes with the type of random number.
Beta: A continuous distribution with finite upper and lower bounds. The two shape parameters can be used to generate a wide variety of density patterns within the two bounds. This distribution is often used as a rough estimate in the absence of data, a distribution of a random proportion, or the time to complete a task. The uniform distribution is a special case of the beta distribution (both shape parameters are equal to one).
Binomial: Outputs an item with a time between arrivals equal to the number of successes (argument (1) – Prob) in a fixed number of independent trials (argument (2) – N). Prob is a real number (the fraction of the population with the desired characteristic) and N is an integer. This distribution is used to show the number of defective items in a batch of size N, the probability of error in the transmission of a message consisting of a specific number of bits, or the probability that a specified number of people will recover from a rare blood disease.
Changes to interarrival time occur immediately: If this choice is selected, new values at the 1, 2, 3, or V connectors will cause the generator to immediately recalculate when the next item is to be generated based on these new values. If this choice is NOT selected, the new values will be used only after the NEXT item has been generated.
Constant: A constant number.
Cost per item: Cost added to the \"_cost\" attribute of each item that is created by the Generator block.
Discrete: For the Empirical distribution only. The data table will be used as discrete probabilities of the values given in the Time column. This means that the values listed in the Time column are the exact intervals that will occur between arrivals.
Empirical table: This table is to be used for the Empirical distribution only. You may import the values through the Clipboard, with the commands in the Edit menu, with the Import Data command from the File menu, or type them in directly. The probabilities need only have the proper relative values since Extend scales them automatically. The Time column contains the various time intervals between arrivals. Probability describes the chance a value will occur.
Erlang: Outputs items approximately every (1) Mean time units with a wide range of outcomes depending on the value of argument (2), “k”. This distribution is used in telephone traffic and queueing theory and is useful for allocating tasks. The value of k should be an integer. Like the Weibull, the curve approximates other distributions depending on the value of its Mean and especially the value of k. A k of 1 causes the curve to resemble the exponential distribution while larger values tend to a normal distribution.
Exponential: A distribution shaped like a decaying exponential. This choice outputs an item approximately every (1) Mean time units, where Mean is a nonnegative real number. However, the distribution is skewed to the left, so it is more likely that the time between arrivals will be between 0 and the Mean than between the Mean and two times the Mean. This distribution is the one most often used in business processes, service industries, and queuing theory. It is used to describe the time between customer arrivals, telephone calls, or the receipt of orders. For instance, a Mean of 6 provides that one item will arrive about every 6 time units. Argument (2) is unused. Note that a Poisson distribution is the inverse of an exponential distribution. To specify a Poisson arrival rate, use an exponential distribution to show the interarrival times. To do this, set the mean of the exponential distribution to 1/Poisson arrival rate.
Gamma: A continuous distribution bounded by zero at the left and unbounded on the right. The exponential and erlang are special cases of the gamma distribution. Because of its flexibility, the gamma distribution can be used for a wide variety of purposes including: interarrival times, time to complete a task, or lifetimes.
Geometric: A discrete distribution bounded by zero on the left and unbounded on the right. It can be defined as the number of failures before the first success in a series of trials. In shape, it is similar to the exponential distribution. Traditional uses include inventory demand and the number of items inspected before the first defective item is found.
HyperExponential: A distribution also used in telephone traffic and queueing theory given its (1) Mean. It perturbs the Exponential distribution in a opposite way to the Erlang. (2) s, (0>1) ranges from 0 to 1 with 0.5 giving an Exponential distribution.
Integer, uniform: Outputs an integer that includes the end values (1) Min and (2) Max.
Interpolated: For the Empirical distribution only. The probability distribution will be interpolated between the data points.
Item will appear as: All items exiting this block will be represented by the animation picture defined in the popup menu.
LogNormal: Natural log of the variable that follows the gaussian or bell curve with the given (1) Mean and (2) Std Dev (standard deviation). This distribution outputs an item approximately every Mean time units, where the time between arrivals is always greater than 0 and is skewed so that most of the occurrences are near the minimum value (positive skew). Lognormal is often appropriate for multiplying processes, while the Normal is best for additive processes. This distribution is widely used in business for security or property valuation, such as the rate of return on stock or real estate returns.
Maximum number of items generated: If this choice is selected, the generator will only generate the number of items entered.
No item at time zero: If this choice is selected, the first item will be output strictly based on the distribution. For example, you would choose to not have the Generator output its first item at time 0 if you use the Generator to activate the down connector on a Machine or Station from the Manufacturing library. If this box is not selected, the Generator will output its first item at time 0.
Normal: Gaussian or bell curve with the given (1) Mean and (2) Std Dev (standard deviation). This choice outputs an item approximately every Mean time units, where the time between arrivals is as likely to be above the mean as below it. This distribution is most often used when events are due to natural occurrences, rather than manmade processes. The Mean is specified as a real number and the standard deviation is specified as a nonnegative real number. The larger the standard deviation, the wider the spread of values around the mean. For example, given a mean of 6, if you expect 68% of the numbers to fall within ±4 (that is, between 2 and 10), enter a Std Dev of 4. This is calculated as 4/1, where 1 represents 1 standard deviation width of values (68%). However, if you expect that 96% of the numbers, or 2 standard deviations, will fall within that same range, enter a Std Dev of 2. This is calculated as 4/2.
Pearson type V: A distribution typically used to represent the time required to complete some task. A continuous distribution bounded by zero on the left and unbounded on the right. The density takes on shapes similar to lognormal, but can have a larger \"spike\" close to x = 0.
Pearson type VI: A distribution typically used to represent the time required to complete some task. A continuous distribution bounded by zero on the left and unbounded on the right.
Plot: Plots N items based on the selected distribution in a histogram.
Plot Table: Plots the outline of the Empirical distribution specified in the data table.
Poisson: A discrete distribution which typically describes the number of events per time unit based on a (1) Mean rate. This distribution is rarely used to specify an interarrival time. Instead, to model the equivalent of a Poisson arrival rate, use an Exponential distribution to specify the interarrival time. The mean value of the Exponential distribution should be 1/mean of the poisson distribution.
Real, uniform: Outputs a real number that is between the end values (1) Min and (2) Max.
Stepped: For the Empirical distribution only. The data table will be used as probabilities of ranges of data. The Time column defines the low end of the bin while the next range value defines the upper end. Stepped distributions require that the last set of points define the upper end of the distribution. For this reason the probability level of the last and next to last points must be equal. If this is not in the data, an additional point will be added onto the data.
Time units: If a time unit other than \"Generic\" is selected in the Simulation Setup, a specific time unit may be selected to define the interarrival time. The asterisk (*) after a time unit indicates that it is the model default and will always be the same as the time units selected in the Simulation Setup.
Total cost: The cost associated with creating the items (Cost per item * total number of items created).
Triangular: Outputs an item every N time units, where N is a real (decimal) number greater than or equal to the real number selected for argument 1 (the minimum) and less than or equal to the real number selected for argument 2 (the maximum) with the added provision that N tends towards its most likely, or modal value. You would use this distribution to specify the lowest possible time between arrivals (min), the greatest possible time between arrivals (max), and the most likely time between arrivals (most likely). The actual performance of this distribution will be similar to the normal distribution with the exception that it can be skewed (if the most likely value is specified to the left or right) and that there is no possibility of outlying values. Note that the most likely value is the mode and not the mean (or average). To determine the mean of the triangular distribution, sum the minimum, maximum, and most likely values and divide by 3.
Use block seed: Sets the value for the random number seed used by this block. If this is not checked, the seed is the block number(a unique identifier for each block) + 1. In most cases, each block which generates random numbers should have its own, unique seed value. The Statistics library (included with the Manufacturing or BPR libraries) contains the Random Seed Control block which can check for duplicate seed values.
Value of item (V): The value on each item generated. This is overridden by the V connector. This can be used for controlling down, change, or select connectors.
Waiting cost per time unit: The cost added to the item per unit of waiting time (storage costs). The time units used to define this cost rate should be consitent with the time units used throughout the model. The cost is calculated in the queues and added to the \"_cost\" attribute for each item.
Weibull: The Weibull distribution can assume the properties of other distributions (such as the Exponential or Rayleigh) depending on its (1) Scale and (2) Shape arguments, both of which are nonnegative real numbers. It is commonly used to describe product life cycles or the time to complete tasks. The curve of the distribution changes considerably depending on the value of Scale (sometimes known as alpha) and especially the value of Shape (sometimes known as beta). The Shape variable should be greater than 0. For example, given a Scale of 1 and a Shape of 1, the Weibull is essentially an exponential distribution.
Use shift: Selects the shift schedule for this block. The shift can be either ON/OFF.
Connectors
1: Value of parameter 1. If connected, this overrides the (1) dialog parameter.
2: Value of parameter 2. If connected, this overrides the (2) dialog parameter.
3: Value of parameter 3. If connected, this overrides the (3) dialog parameter.
V: The value on each item generated. This overrides the Value of item (V) option in the dialog. This can be used for controlling down, change, or select connectors.
The output is the discrete event item.
Animation
The icon flashes near the output connector each time an item is generated. 
  
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1 day1 hr confidence: provides items for a discrete event simulation at specified interarrival times. provee artículos para una discreta simulación de evento en tiempos específicos de llegada intercalad
Explanation: an option
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