Represents the arrival of items from outside the scope of a model at a constant or a random rate. Select the distribution for providing inputs from the dialog. The first “(1)” and second “(2)” parameters (arguments) for the specified distribution are on the right side of the dialog; "unused" means that there is no second argument for the chosen distribution.
Note that the Import block outputs items based on the time between arrivals of items. For instance, an exponential distribution with a mean of 4 would provide items approximately every 4 time units. You can also specify an attribute value, the priority, and the Value (the size of a batch) for the items output.
The Import block always produces items at the rate specified, so it is recommended that a Stack or a Repository block follow the Import to store the items until they can be processed.
NOTE: Each time the simulation is run, the random numbers that are generated will be different, unless a seed is specified in the Simulation Setup dialog. It is important to remember that since the generated numbers are random, for short runs they may not give you the exact results you expect due to the small sample population. For example, if you choose the “Integer, uniform” distribution with a minimum of 2 and a maximum of 3, and the simulation only runs for 20 time units, you may not get 2 exactly 50% of the time.
Dialog Choices (also see Distribution Choices, below):
(1) and (2): Variables (arguments) that are used to define or dynamically vary the rate of arrival of items. These variables are used to calculate the time between arrivals of items. (1) is the first argument, (2) is the second argument (if used). The arguments change based on the chosen distribution. For example, the Normal distribution has “Mean” for the first argument, and “Std. Dev.” (standard deviation) for the second argument.
Most likely value for the triangular distribution. This is the mode, or most likely value, of the triangular distribution. If the most likely value is left blank, Extend will create a symmetrical triangular distribution by setting the most likely value half way between the min and the max.
No item at time zero: Specifies that the first input to the simulation will occur randomly, regardless of when the simulation begins. This is unchecked by default, forcing an item to be generated at the beginning of each simulation run (simulation time zero).
Set attribute, priority, or number of items (optional): To assign an attribute, a priority, or a number of items (Value) to all items as they leave the block, enter the information in the dialog. The attribute value, priority, and number of items can be set dynamically using the A, P, and V input connectors, which override the dialog values.
• Attr. name: The name of the attribute with the value below.
• Attr. value: The value for the named attribute. This is overridden by the A input.
• Priority: The priority for the item. Lower numbers (including negative numbers) mean higher priority.
• # of items (V): The number of clones in the batch generated (also known as the Value of the item). The default is 1, indicating that there is only one item in the batch. You would usually not change this to a number other than 1, unless you want to show a batch of more than one identical items arriving at each event. For example, if you use the Constant distribution and 5 items are to be input every 60 (simulated) minutes, set the “(1) Constant” option to 60, and set the “# of items” option to 5. If you set “# of items” to a number greater than 1, all clones in the batch have the same attributes and priorities.
Distribution Choices:
Constant: This is the default distribution selection. It outputs an item every Constant (argument 1) time units. Use this distribution when there is exactly the same amount of time between arrivals of items. The Constant argument specifies how much time there is between items arriving; the default is 1 simulation time unit. Argument (2) is unused. For example, if one item is to be input every 60 (simulated) minutes, set the "(1) Constant" variable to 60 and do not change any of the other settings.
Erlang: Outputs items approximately every (1) Mean time units with a wide range of outcomes depending on the value of the second argument, "k". This distribution is used in telephone traffic and queueing theory. The variable k should be an integer. Like the Wiebull, the curve approximates other distributions depending on the value of its Mean and especially the value of k. A k of 1 resembles 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 non-negative real number. However, the distribution is positively skewed (longer tail on the right), 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.
Integer, uniform: Outputs an item every N time units, where N is an integer (whole) number greater than or equal to the integer selected for argument 1 and less than or equal to the integer selected for argument 2. In this distribution, all the integers between the minimum and maximum are equally likely to occur. For instance, you would use this distribution to show orders arriving every 1 to 3 minutes, or any other best case/worst case scenario.
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 man-made processes. The Mean is specified as a real number and the standard deviation is specified as a non-negative real number. The larger the standard deviation, the wider the spread of values around the mean. For example, given a mean of 6 and the expectation that 68% of the numbers will occur ±4 (that is, that 68% of the values fall between 2 and 10), you would 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.
Real, uniform: 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 and less than or equal to the real number selected for argument 2. In this distribution, all the real values between the minimum and maximum are equally likely to occur. For instance, you would use this distribution to show orders arriving every 1.32 to 2.87 minutes, or any other best case/worst case scenario.
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.
Weibull: This 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 non-negative 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. However, given a Scale of 1 and a Shape of 2 the curve resembles a skewed normal distribution.
Connectors:
The item output connector provides items based on the distribution selected. The distribution and its arguments determine how much time occurs before the next item is input.
1, 2: The value of arguments (1) and (2). If these are connected, their values will override the values set in the dialog.
A: The value for the attribute named in the dialog. If connected, this overrides the "Attr. value" set in the dialog.
P: The priority value. The lower the number (including negatives), the higher the priority. If connected, this input overrides the "Priority" set in the dialog.
V: The number of item clones to be input into the simulation as each item is generated (this is technically known as the Value of the item). If connected, this overrides the "# of items" set in the dialog.
Animation:
A green circle appears near the output connector each time an item is generated and picked up by the next block. The circle is red if the item is generated but is not picked up (is lost to the model).
