Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Tan, Pheng Hak
- Economic-Statistical Design of Synthetic X Chart for Monitoring Process Mean under Non-Normality
Abstract Views :188 |
PDF Views:0
Authors
Affiliations
1 Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, Sarawak, MY
2 Institute of Sensors, Signals and Systems, School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Putrajaya, Selangor, MY
1 Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, Sarawak, MY
2 Institute of Sensors, Signals and Systems, School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Putrajaya, Selangor, MY
Source
Indian Journal of Science and Technology, Vol 11, No 45 (2018), Pagination: 1-9Abstract
Objectives: In this study, the economic-statistical design of the synthetic X chart is developed for monitoring the non-normal process data. Methods/Analysis: The Burr distribution is employed as a general model of the process distribution. In this economic-statistical model, the optimal design parameters of the synthetic X chart are determined, such that the cost function is minimized, for the given values of the cost and process parameters. An example is presented to illustrate the solution procedure of the economic-statistical design for the synthetic X chart under non-normality. Findings: A sensitivity analysis on the cost and process parameters is presented. The results of the sensitivity analysis conclude that a cost cost can be reduced by decreasing one of the values in λ, C0, C1, Y, W, b, c, e and T1 or by increasing one of the values in δ and T2. In comparison with the variable parameters X chart, the synthetic X chart has better economic performance. Novelty/Improvement: To investigate the economic and statistical performances of the synthetic X chart when the measurements are not normally distributed.Full Text
References
- Burr IW. Cumulative frequency functions. The Annals of Mathematical Statistics. 1942; 13(2):401–7. https://doi.org/10.1214/aoms/1177731607
- Noorossana R, Toosheghanian M, Gazaneh FM. Using genetic algorithm and response surface methodology for statistically constrained optimization of VSI control charts under multiple assignable causes and non-normality. Int J Adv Manuf Technol. 2013; 67(9-12):2325–42. https://doi.org/10.1007/s00170-012-4653-7
- Seif A, Faraz A, Saniga E. Economic statistical design of the VP control charts for monitoring a process under non-normality. International Journal of Production Research. 2015; 53(14):4218–30. https://doi.org/10.1080/00207543.2014.98 6298
- Heydari AA, Moghadam MB, Eskandari F. An extension of Banerjee and Rahim model in economic and economic statistical designs for multivariate quality characteristics under Burr XII distribution. Commun Stat Theory Methods. 2017; 46(16):7855–71. https://doi.org/10.1080/03610926.2016.1140782
- Patil SH, Shirke DT. Economic design of moving average control chart for non-normally distributed data:A comparative study. Int Trans Oper Res. 2017; 28(1):98–120. https:// doi.org/10.1504/IJOR.2017.080598
- Faraz A, Chalaki K, Saniga EM, Heuchenne C. The robust economic statistical design of the Hotelling’s T2 chart. Commun Stat Theory Methods. 2016; 45(23):6989–7001. https://doi.org/10.1080/03610926.2014.972574
- Costa A, Fichera S. Economic statistical design of ARMA control chart through a modified fitness-based self-adaptive differential evolution. Computers and Industrial Engineering. 2017; 105:174–89. https://doi.org/10.1016/j.cie.2016.12.031
- Tavakoli M, Pourtaheri R, Moghadam MB. Economic and economic-statistical designs of VSI Bayesian control chart using Monte Carlo method and ABC algorithm. J Stat Comput Simul. 2017; 87(4):766–76. https://doi.org/10.1080/00949655.2016.1225069
- Naderi MH, Moghadam MB, Seif A. Economic statistical design of the T2 control chart under the Weibull shock model with multiple assignable causes. J Stat Comput Simul. 2018; 88(1):1–27. https://doi.org/10.1080/00949655. 2017.1376329
- Pasha MA, Moghadam MB, Fani S, Khadem Y. Effects of quality characteristic distributions on the integrated model of Taguchi’s loss function and economic statistical design of control charts by modifying the Banerjee and Rahim economic model. Commun Stat Theory Methods. 2018; 47(8):1842–55. https://doi.org/10.1080/03610926.2017.1328512
- Safe H, Kazemzadeh RB, Kanani YG. A Markov chain approach for double-objective economic statistical design of the variable sampling interval control chart. Commun Stat Theory Methods. 2018; 47(2):277–88. https://doi.org/10.1080/03610926.2016.1235197
- Lee MH, Khoo MBC. Economic-statistical design of control chart with runs rules for correlation within sample. Commun Stat Simul Comput. Forthcoming 2017. PMid: 29276335.
- Lorenzen TJ, Vance LC. The economic design of control charts: A unified approach. Technometrics. 1986; 1:3–10. https://doi.org/10.1080/00401706.1986.10488092
- Wang RC, Chen CH. Economic statistical np-control chart designs based on fuzzy optimization. International Journal of Quality and Reliability Management. 1995; 12(1):82–92. https://doi.org/10.1108/02656719510076276
- Keats JB, Castillo ED, Collani EV, Saniga EM. Economic modeling for statistical process control. Journal of Quality Technology. 1997; 29(2):144–7. https://doi.org/10.1080/00224065.1997.11979744
- Lee PH, Torng CC, Liao LF. An economic design of combined double sampling and variable sampling interval control chart. International Journal of Production Economics. 2012; 138(1):102–6. https://doi.org/10.1016/j.ijpe.2012.03.006
- McWilliams TP, Saniga EM, Davis DJ. Economic-statistical design of and R or and S charts. Journal of Quality Technology. 2001; 33(2):234–41. https://doi.org/10.1080/0 0224065.2001.11980070
- Ghanaatiyan R, Amiri A, Sogandi F. Multi-objective economicstatistical design of VSSI-MEWMA-DWL control chart with multiple assignable causes. Journal of Industrial and Systems Engineering. 2017; 10:34–58.
- Lu SL, Huang CJ. Statistically constrained economic design of maximum double EWMA control charts based on loss functions. Qual Technol Quant Manag. 2017; 14(3):280–95. https://doi.org/10.1080/16843703.2016.1208940
- Pasha MA, Moghadam MB, Fani Sh. A generalized approach to economic design of control charts: A discussion on implementing. Commun Stat Theory Methods. 2017; 46(21):10495–506. https://doi.org/10.1080/03610926 .2016.1236963
- Safe H, Kazemzadeh RB, Kanani YG. A Markov chain approach for double-objective economic statistical design of the variable sampling interval control chart. Commun Stat Theory Methods. 2018; 47(2):277–88. https://doi.org/10.1080/03610926.2016.1235197
- Seif A. A new Markov chain approach for the economic statistical design of the VSS T2 control chart. Commun Stat Simul Comput. Forthcoming 2017. https://doi.org/10.1080/03610918.2017.1377238
- Dodge Y, Rousson V. The complications of the fourth central moment. Am Stat. 1999; 53(3):267–9. https://doi.org/10.2307/2686108
- Burr IW. Parameters for a general system of distributions to match a grid of a3 and a4. Communications in Statistics. 1973; 2(1):1–21. https://doi.org/10.1080/03610927308827052
- Wu Z, Spedding TA. Implementing synthetic control charts. Journal of Quality Technology. 2000; 32(1):75–8. https://doi.org/10.1080/00224065.2000.11979973
- Yeong WC, Khoo MBC, Wu Z, Castagliola P. Economically optimum design of a synthetic chart. Quality and Reliability Engineering International. 2012; 28(7):725–41. https://doi.org/10.1002/qre.1264
- Lin YC, Chou CY. Robustness of the variable sample size and control limit chart to non-normality. Commun Stat Theory Methods. 2005; 34(3):721–43. https://doi.org/10.1081/STA-200052136