An Extension of Fuzzy WV Control Chart based on \(\alpha\)-Level Fuzzy Midrange
DOI:
https://doi.org/10.26713/cma.v7i3.427Keywords:
Fuzzy, \(\alpha\)-cut, \(\alpha\)-level fuzzy midrangeAbstract
Control chart is one of the most important tools in statistical process control (SPC) that leads to improve quality processes and ensure the required quality levels. The usual assumption for designing a control chart is that the data or measurement should have a normal distribution. However, this assumption may not be true for some processes, there are some factors that cause an uncertainty data such as human, measurement device or environmental conditions. Therefore, the purposes of this research are to study, develop and compare the efficiency of fuzzy weighted variance (FWV) control charts which the data has non-normal distribution as Weibull, gamma and Chi-squared. FWV control charts use fuzzy set theory to help in solving the uncertainty data. The random variable for the experiment will be transformed to fuzzy control chart by using triangular membership function. Finally, the performance and comparative efficiency of the FWV control charts are measured in term of average run length (ARL) by Monte Carlo simulation technique.Downloads
References
D.S. Bai and I.S. Choi, (bar{x}) and (R) control charts for skewed populations, J. Quality. Technology 27 (1995), 120–131.
F. Choobineh and J.L. Ballard, Control limits of QC charts for skewed distribution using weighted variance, IEEE Transactions on Reliability 36 (1987), 473–477.
A. Pongpullponsak, W. Suracherkiati and R. Intaramo, The Comparison of Efficiency of Fuzzy x Control Chart by Weighted Variance Method, Scaled Weighted Variance Method, Empirical Quantiles Method and Extreme-value Theory for Skewed Populations, KMUTT (2012).
A. Pongpullponsak, W. Suracherkiati and P. Kriweradechachai, The comparison of efficiency of control chart by weighted variance method, Nelson method, Shewhart method for Skewed populations, in Proceeding of 5th Applied Statistics Conference of Northern Thailand, Chiang Mai, Thailand (2004).
A. Pongpullponsak, W. Suracherkiati and R. Intaramo, The comparison of efficiency of control chart by weighted variance method, scaled weighted variance method, empirical quantiles method and extreme-value theory for skewed populations, J. Kmitl Science 6 (2006), 456–465.
S. Senturk and N. Erginel, Development of fuzzy and (widetilde{bar{X}})-(widetilde{R}) and (widetilde{bar{X}})-(widetilde{S}) control charts using (alpha)-cuts, Information Science 179 (2009), 1542–1551.
W.A. Shewhart, Economic Control of Quality of Manufactured Product, Van Nostrand, New York (1931).
M.B. Vermaat, A. Ion Roxana and J.M.M. Ronald, A comparison of Shewhart individual control charts based on normal, non-parametric, and extreme-value theory, Quality and Reliability Engineering International 19 (2003), 337–353.
L.A. Zadeh, Fuzzy sets, Information and Control 8 (1965), 338–353.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CCAL that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
