The Mean Waiting Time Estimation of Queueing Network via Biased Control Variates

• Main Authors: Yu-Ching Lin, 林妤璟
• Other Authors: Ying-chieh Yeh
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/v7m325

碩士 === 國立中央大學 === 工業管理研究所 === 106 === For the queueing system, generally pay close attention on two issues: the waiting time and the queueing length, because these can show the important information for manager that is where is the delay problem. The M/M/s model has been extended for many years. For GI/G/s queue, the distribution conditions for arrival process are relaxed, making the queueing model more realistic, so it is difficult to estimate the congestion measurement, and there is not yet an exact estimation model. As a result, many researcher have continued to conduct more in-depth studies on the numerical and parameter estimates of the GI/G/s queue. In Kimura’s research, based on the QNA (Queue Network Analyzer), the conversion parameters were set to propose various expansion models, and the difference between the expected waiting time of the model and the actual average waiting time was used as the judgment of the accuracy rate, but he didn’t mention about the variance and the estimated statement is stable or not. In simulation experiments, studies have shown that variate reduction techniques can help to make simulation estimates more accurate and make experiments more efficient. This study uses one of the methods: Biased Control Variates, and it be used highly approximated pairs of approximations. The adjustment of the original model estimate can improve the variation of the pre-estimation parameter and make the estimator more stable than the original estimation model. Therefore, in this study, the approximation model of the average waiting time of QNA is combined with the control mutation technique, and the accuracy of the extended model after QNA is compared.