Statistical Monitoring of Queuing Networks
Queuing systems are important parts of our daily lives and to keep their operations at an efficient level they need to be monitored by using queuing Performance Metrics, such as average queue lengths and average waiting times. On the other hand queue lengths and waiting ti...
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ndltd-USF-oai-scholarcommons.usf.edu-etd-87312019-10-04T05:06:14Z Statistical Monitoring of Queuing Networks Kaya, Yaren Bilge Queuing systems are important parts of our daily lives and to keep their operations at an efficient level they need to be monitored by using queuing Performance Metrics, such as average queue lengths and average waiting times. On the other hand queue lengths and waiting times are generally random variables and their distributions depend on different properties like arrival rates, service times, number of servers. We focused on detecting the change in service rates in this report. Therefore, we monitored queues by using Cumulative Sum(CUSUM) charts based on likelihood ratios and compared the Average Run Length values of different service rates. 2018-10-26T07:00:00Z text application/pdf https://scholarcommons.usf.edu/etd/7534 https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=8731&context=etd Graduate Theses and Dissertations Scholar Commons CUSUM Charts Jackson's networks Queuing analysis Markov Chain Statistical Process Control Industrial Engineering |
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CUSUM Charts Jackson's networks Queuing analysis Markov Chain Statistical Process Control Industrial Engineering |
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CUSUM Charts Jackson's networks Queuing analysis Markov Chain Statistical Process Control Industrial Engineering Kaya, Yaren Bilge Statistical Monitoring of Queuing Networks |
description |
Queuing systems are important parts of our daily lives and to keep their operations at an efficient level they need to be monitored by using queuing Performance Metrics, such as average queue lengths and average waiting times. On the other hand queue lengths and waiting times are generally random variables and their distributions depend on different properties like arrival rates, service times, number of servers. We focused on detecting the change in service rates in this report. Therefore, we monitored queues by using Cumulative Sum(CUSUM) charts based on likelihood ratios and compared the Average Run Length values of different service rates. |
author |
Kaya, Yaren Bilge |
author_facet |
Kaya, Yaren Bilge |
author_sort |
Kaya, Yaren Bilge |
title |
Statistical Monitoring of Queuing Networks |
title_short |
Statistical Monitoring of Queuing Networks |
title_full |
Statistical Monitoring of Queuing Networks |
title_fullStr |
Statistical Monitoring of Queuing Networks |
title_full_unstemmed |
Statistical Monitoring of Queuing Networks |
title_sort |
statistical monitoring of queuing networks |
publisher |
Scholar Commons |
publishDate |
2018 |
url |
https://scholarcommons.usf.edu/etd/7534 https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=8731&context=etd |
work_keys_str_mv |
AT kayayarenbilge statisticalmonitoringofqueuingnetworks |
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1719260352938508288 |