2. Introduction
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Input data includes gathering, studying, and utilizing input data
in the simulation method
The collection may be gathered from any source.
A study of input data shows the theoretical distribution of data
the practitioner only gathers a sample of the actual data
distribution when collecting data..
3. Collecting Input Data
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There are many ways to collect input data like the following:
Historical records
Manufacturer specifications
Vendor claims
Operator estimates
Management estimates
Automatic data capture
Direct observation
4. Collecting Input Data
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Input data may be collected manually or with the assistance of
electronic devices
It is the most difficult part of the simulation process
While colleting input data, there are different classifications of data
5. Classification of Data
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There are two methods for the classification of data:
Deterministic or probabilistic
Discrete or continuous
6. Deterministic /Probabilistic Data
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Deterministic Data
Deterministic data are those in which the event affecting the
data occurs consistently or predictably.
Probabilistic Input Data
A probabilistic process does not occur with the same type of
regularity.
This implies that since the value of this type of data never
changes, it only has to be gathered once.
7. Discrete/Continuous Data
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Discrete Data
It can take only certain values. Usually, this means a whole number.
The number of students in class is an example
Continuous Data
It can take any value in the observed range. This means that
fractional numbers are a definite possibility
Height of children is an example
8. Input Data Distributions
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Bernoulli Distribution:
Models a random occurrence with one of two possible
outcomes
Frequently referred to as a success or failure
Uniform Distribution:
It can be used as a first cut for modeling the input data
It may be either discrete or continuous
Exponential Distribution :
Commonly utilized in conjunction with interarrival processes
Random no. of entities will arrive within a specific time
9. Input Data Distributions
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Triangle Distribution:
Used in situations where the practitioner does not have complete
knowledge about the system
It has only three parameters:
i) Minimum Possible Value
ii) Most Common Value
iii) Maximum Possible Value
10. Less Common Distributions
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Geometric distributions:
The geometric distribution gives the probability of achieving success after
N number of failures
It is discrete which means that distribution must be whole number
Weibull Distribution:
The Weibull distribution is often used to represent distributions that cannot
have values less than zero
It has two parameters
11. Analyzing Input Data
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Graphics Approach:
Graphic approach is the most fundamental approach to attempting to fit
input data
It consists of visual qualitative comparison between actual and
theoretical data distribution
Chi-Square:
The chi-square test is based on the comparison of the actual number of
observations
versus the expected number of observations
Commonly accepted as preferred goodness fit technique
Kolmogorov–Smirnov:
The KS test should be utilized only when the number of data points is
12. Software Implementations for
Data Fitting
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Fitting a significant no. of observed data sets to theoretical
distributions is a time consuming task
For this purpose, practitioners use data-fitting software
The following two are frequently used to carry out this function:
1. Arena input analyzer
2. Expert fit
13. Arena input analyzer
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Input analyzer is part of ARENA simulation software package
available from Rockwell software
It has the capability to:
1. Determine the quality of fit of probability distribution functions to
input data
2. Examine a total of 15 distributions for data fitting
3. Calculate Chi-square, KS and square error tests
4. Generate high-quality data plots
14. Expert Fit
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This software is available through Averil M. Law & Associates
This software has the capability to:
1. Automatically determine best probability distribution for data
sets
2. Fits 40 distributions
3. Conduct Chi-sqaure, KS and Anderson-Darling goodness of fit
tests
4. Provide high-quality plots
5. Analyze a large no. of data sets in batch mode