Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Comparing Short and Long-Memory Charts to Monitor the Traffic Intensity of Single Server Queues

Comparing Short and Long-Memory Charts to Monitor the Traffic Intensity of Single Server Queues AbstractThe traffic intensity (ρ) is a vital parameter of queueing systems because it is a measure of the average occupancy of a server.Consequently, it influences their operational performance, namely queue lengths and waiting times. Moreover, since many computer, production and transportation systems are frequently modelled as queueing systems, it is crucial to use control charts to detect changes in ρ. In this paper, we pay particular attention to control charts meant to detect increases in the traffic intensity, namely: a short-memory chart based on the waiting time of the n-th arriving customer; two long-memory charts with more sophisticated control statistics, and the two cumulative sum (CUSUM) charts proposed by Chen and Zhou (2015). We confront the performances of these charts in terms of some run length related performance metrics and under different out-of-control scenarios. Extensive results are provided to give the quality control practitioner a concrete idea about the performance of these charts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economic Quality Control de Gruyter

Comparing Short and Long-Memory Charts to Monitor the Traffic Intensity of Single Server Queues

Loading next page...
 
/lp/de-gruyter/comparing-short-and-long-memory-charts-to-monitor-the-traffic-cCRYnTtBi7
Publisher
de Gruyter
Copyright
© 2019 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1869-6147
eISSN
2367-2404
DOI
10.1515/eqc-2018-0026
Publisher site
See Article on Publisher Site

Abstract

AbstractThe traffic intensity (ρ) is a vital parameter of queueing systems because it is a measure of the average occupancy of a server.Consequently, it influences their operational performance, namely queue lengths and waiting times. Moreover, since many computer, production and transportation systems are frequently modelled as queueing systems, it is crucial to use control charts to detect changes in ρ. In this paper, we pay particular attention to control charts meant to detect increases in the traffic intensity, namely: a short-memory chart based on the waiting time of the n-th arriving customer; two long-memory charts with more sophisticated control statistics, and the two cumulative sum (CUSUM) charts proposed by Chen and Zhou (2015). We confront the performances of these charts in terms of some run length related performance metrics and under different out-of-control scenarios. Extensive results are provided to give the quality control practitioner a concrete idea about the performance of these charts.

Journal

Economic Quality Controlde Gruyter

Published: Jun 1, 2019

References