Economic- Statistical Design of MAX EWMAMS Chart under Measurements Error and Multiple Measurements

Document Type : Research Paper

Authors

1 Department of Industrial of Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

2 Department of Industrial Engineering, Kurdestan University, Kurdestan, Iran

Abstract

     In this paper, the economic-statistical design of the Max EWMAMS control chart under measurement errors and multiple measurements for joint monitoring of mean and variability of the process is investigated. The traditional approach for monitoring mean and variance of the quality characteristic is using two separate control charts. This approach leads to an increase in the probability of Type I error. To overcome this problem, researchers have proposed control charts for joint monitoring of mean and variability of the process. Also in practice, the measurement errors exist in the sampling process. The sampling with multiple measurements is a way to reduce the deficiency of the measurement error and increasing power of the control chart. However, the multiple measurements cause to increase the sampling costs. Hence, this factor should be considered in the economic-statistical design of control charts as well. In the proposed cost model, the Lorenzen-Vance cost function is developed and a genetic algorithm is applied to obtain model parameters that minimize cost function. Finally, the performance of the proposed model is evaluated by a numerical example.

Keywords


[1] Chen, G., Cheng, S.W., Xie, H. (2001). “Monitoring process mean and variability with one EWMA chart”, Journal of Quality Technology, 33: 223-233.
[2] Ostad sharif Memar, A., Akhavan Niaki, S.T. (2011). “The Max EWMAMS control chart for joint monitoring of process mean and variance with individual observations”, Quality and Reliability Engineering International, 27: 499-514.
[3] Costa, A. F. B., Rahim M. A. (2004). “Monitoring process mean and variability with one non-central chi-square chart”, Journal of Applied Statistics, 31: 1171-1183.
[4] Farughi, H., Tunekaboni, S., Daryabari, S. A. (2014). A review of single scheme simultaneous control charts for the mean and variance. Tenth International Industrial Engineering conference, Iran.
[5] Cheng, S. W., Thaga, K. (2006). “Single variables control charts: an overview”, Quality and Reliability Engineering International, 22: 811-820.
[6] McCracken, A. K., Chakraborti, S., (2013). “Control charts for joint monitoring of mean and variance: an overview”, Quality Technology and Quantitative Management, 1: 17-36.
[7] Saghaei, A., Fatemi Ghomi S. M. T., Jaberi, S. (2014). “The economic design of simple linear profiles”, International Journal of Industrial Engineering, 24: 405-412.
[8] Amiri A., Bashiri M., Maleki M.R., Moghaddam A.S. (2014). “Multi-objective Markov-based economic-statistical design of EWMA control chart using NSGA-II and MOGA algorithms”, International Journal of Multi criteria Decision Making, 4: 332-347.
[9] Montgomery, D., Torng, J.C., Cochran J.K., Lawrence, F.P. (1995). “Statistically constrained economic design of the EWMA control chart”, Journal of Quality Technology, 37: 250-256.
[10] Lorenzen, T.J., Vance, L.C. (1986). “The economic design of control charts: a unified approach”, Technometrics, 28: 3-10.
[11] Serel, D.A., Moskowitz, H. (2008). “Joint economic design of EWMA control charts for mean and variance”, European Journal of Operational Research, 184(1): 157-168.
[12] Serel, D.A. (2009). “Economic design of EWMA control charts based on loss function”, Mathematical and Computer Modelling, 49: 745-759.
[13] Bennett, C. A. (1954). “Effect of measurement error on chemical process control”, Industrial Quality Control, 10: 17-20.
[14] Linna, K. W., Woodall, W. H. (2001). “Effect of measurement error on Shewhart control charts”, Journal of Quality Technology, 33: 213-222.
[15] Linna, K. W., Woodall, W. H. (2001). “The performance of multivariate control charts in the presence of measurement error”, Journal of Quality Technology, 33: 335-349.
[16] Maravelakis, P. E. (2007). “The effect of measurement error on the performance of the CUSUM control chart”. IEEE International Conference on Industrial Engineering and Engineering Management (1399-1402).
[17] Abbasi, S. A. (2010). “On the performance of   EWMA chart in the presence of two-component measurement error”, Quality Engineering, 22: 199-213.
[18] Maleki, M.R., Amiri, A., Castagliola, P. (2017). “Measurement errors in statistical process monitoring: A literature review”, Computers and Industrial Engineering, 103: 316-329.
[19] Daryabari, S.A., Hashemian, S.M., Keyvandarian, A. and Shekary A, M. (2017). “The Effects of Measurement Error On The MAX-EWMAMS Control Chart”, Communications in Statistics-Theory and Methods, 46: 5766-5778.
[20] Khati Dizabadi, A., Shahrokhi, M. and Maleki, M.R. (2015). “On the effect of measurement error with linearly increasing‐type variance on simultaneous monitoring of process mean and variability”, Quality and Reliability Engineering International, 32: 1693-1705.
[21] Ross, S.M. (1970). Applied probability models with optimization applications. San Francisco, Holden day.
[22] McWilliams, T.P. (1989). “Economic control chart designs and the in-control time distribution: A sensitivity study”. Journal of Quality Technology, 21: 103–110.
[23] Elsayed, E.A., Chen, A. (1994). “An economic design of [xbar] control chart using quadratic loss function”, The International Journal of Production Research, 32: 873-877.
[24] Chou, C.Y., Chen, C.H., Liu, H.R. (2006). “Economic design of EWMA charts with variable sampling intervals”. Quality and Quantity, l40: 879-96.
[25] بازدار، علی‌اصغر، چالاکی، امیر. (1396). "آزمون زنجیره تغییرات مشخصه‌های کلیدی کیفیت به منظور تشخیص منبع بروز خطا در فرآیندهای تولیدی چندمرحله‌ای"، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، 5: 69-81.