Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an accurate and credible model. Verifying and validating simulation models developers and users of these models, the decision makers using information obtained from the results of these models, and the individuals affected by decisions based on such models are all rightly concerned with whether a model and its results are "correct".
Simulation models are approximate imitations of real-world systems and they never exactly imitate the real-world system. Due to that, a model should be verified and validated to the degree needed for the models intended purpose or application. The verification and validation of simulation model starts after functional specifications have been documented and initial model development has been Verifying and validating simulation models. In the context of computer simulation, verification of a model is the process of confirming that it is correctly implemented with respect to the conceptual model it matches specifications and assumptions deemed acceptable for the given purpose of application.
The objective of model verification is to ensure that the implementation of the model is correct. There are many techniques that can be utilized to verify a model. Including, but not limited to, have the model checked by an expert, making logic flow Verifying and validating simulation models that include each logically possible action, examining the model output for reasonableness under a variety of settings of the input parameters, and using an interactive debugger.
Validation checks the accuracy Verifying and validating simulation models the model's representation of the real system. Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model".
There are many approaches that can be used to validate a computer model. The approaches range
Verifying and validating simulation models subjective reviews to objective statistical tests. One approach that is commonly used is to have the model builders determine validity of the model through a series of tests. Naylor and Finger  formulated a three-step approach to model validation that has been widely followed: Compare the model input-output transformations to corresponding input-output transformations for the Verifying and validating simulation models system.
A model that has face validity appears to be a reasonable imitation of a Verifying and validating simulation models system to people who are knowledgeable of the real world system. Assumptions made about a model generally fall into two categories: Assumptions made about how the system operates and how it is physically arranged are structural assumptions.
For example, the number of servers in a fast food drive through lane and if there is more than one how are they utilized? Do the servers work in parallel where a customer completes a transaction by visiting a single server or does one server take orders and handle payment while the other prepares and serves the order.
Many structural problems in the model come from poor or incorrect assumptions. There must be a sufficient amount of appropriate data to build a conceptual model and validate a model. Lack of appropriate data is often the reason attempts to validate a model fail. A typical error is assuming an inappropriate statistical distribution for the data.
Any outliers in the data should be checked. The model is viewed as an input-output transformation for these tests. The validation test consists of comparing outputs from the system under consideration to model outputs for the same set of input conditions.
Data recorded while observing the system must be available in order to perform this test. The model would be run with the actual arrival times and the model average time in line would be compared with the actual average time spent in line using one or more tests.
Statistical hypothesis testing using the t-test can be used as a basis to accept the model as valid or reject it as invalid. To perform the test a number n statistically independent runs of the model are conducted and an average or expected value, E Yfor the variable of interest is produced.
Decreasing the probability of a type II error is very important. The probability of a type II error is dependent
Verifying and validating simulation models the sample size and the actual difference between the sample value and the observed value.
Increasing the sample size decreases the risk of a type II error. A statistical technique where the amount of model accuracy is specified as a range has recently been developed. The technique uses hypothesis testing to accept a model if the difference between a model's variable of interest and a system's variable of interest is within a specified range of accuracy. The t-test statistic is used in this technique. The hypothesis to be tested is if D is within the acceptable range of accuracy.
The operating characteristic OC curve is the probability that the null hypothesis is accepted when it is true. Risk curves for model builder's risk and model user's can be developed from the OC curves. Comparing curves with fixed sample size tradeoffs between model builder's risk "Verifying and validating simulation models" model user's risk can be seen easily in the risk curves.
Confidence intervals can be used to evaluate if a model is "close enough"  to a system for some variable of interest. An interval, [a,b], is constructed by. If statistical assumptions Verifying and validating simulation models be satisfied or there is insufficient data for the system a graphical comparisons of model outputs to system outputs can be used to make a subjective decisions, however other objective tests are preferable.
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Verification and Validation.
CSE 3. Verification of Simulation Models. Many commonsense suggestions can be given for use in the verification process. 1. Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an.