The purpose of this research is to a treatment the impact of Views outliers to the estimators of a distributed arrival and service to the theory of queues and estimate the distribution parameters depending on the robust estimators, and when he was outliers greatest impact in the process of estimating the both distributions mentioned parameters, it was necessary to use way to test that does these data contain abnormal values or not? it was used the method ( Tukey ) for this purpose and is of the most popular ways to discover the outliers , it shows that there are views abnormal (outliers ) in the estimators of each of the distributional arrival and service, which have a significant impact on the calculation of these estimators have been addressed through the use of ( Robust Estimation Method ) be of the effectiveness and feasibility of robust estimator better than the estimated normal extracted ( MLE ) ( ordinary Maximum Likelihood estimation ), as was the use of the ( weighted Maximum Likelihood estimation )(WMLE) in the estimation process, was best estimate is the robust estimated existence of outliers , which have the greatest impact in the process of improving the efficiency of the performance of the queue system which led to relieve pressure on the service system, which in turn reduces delays for patients.
The key findings of the research is to adopt robust estimators for distributional arrival and service models queues in general because they are working to address the impact of outliers winning in the data.
