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An approximation of average run length using the Markov chain approach of a generally weighted moving average chart to monitor the number of defects |
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| รหัสดีโอไอ | |
| Creator | Saowanit Sukparungsee |
| Title | An approximation of average run length using the Markov chain approach of a generally weighted moving average chart to monitor the number of defects |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2561 |
| Journal Title | Songklanakarin Journal of Science and Technology |
| Journal Vol. | 40 |
| Journal No. | 6 |
| Page no. | 1368-1377 |
| Keyword | closed-form formulae, time-varying chart, time consuming, binomial distribution and stopping times |
| URL Website | http://rdo.psu.ac.th/sjstweb/index.php |
| ISSN | 0125-3395 |
| Abstract | The objective of this research is to propose an approximation average run length (ARL) using the Markov chain approach (MCA) of a generally weighted moving average chart (GWMA) when observations are based on an underlying binomial distribution. The numerical results obtained from the MCA were compared with the results obtained from a Monte Carlo (MC) simulation method and the efficiency of the ARL was measured by CPU time. The performances of the GWMA and exponentially weighted moving average (EWMA) charts were compared in terms of monitoring the change in the process mean as defined by an out-of-control average run length (ARL1). The numerical results showed that the results of the ARL obtained from MCA were in good agreement with the results obtained from MC; however, the MCA took less CPU time than the MC simulation method. Furthermore, the performance of the GWMA chart was superior to the EWMA chart when the magnitudes of change were small (??0.05), otherwise the EWMA performed better than the GWMA chart. |
