Considering Taguchi loss function on statistically constrained economic sum of squares exponentially weighted moving average charts

References

• Sweet AL. Control charts using coupled exponentially weighted moving averages. IIE Trans. 1986;18:26–33. doi: 10.1080/07408178608975326
   Web of Science ®Google Scholar
• Gan FF. Joint monitoring of process mean and variance. Nonlinear Anal. 1997;30:4017–4024. doi: 10.1016/S0362-546X(97)00224-1
   Web of Science ®Google Scholar
• Reynolds MRJr, Stoumbos ZG. Monitoring the process mean and variance using individual observations and variable sampling intervals. J Qual Technol. 2001;33:181–205.
   Web of Science ®Google Scholar
• Domangue R, Patch SC. Some omnibus exponentially weighted moving average statistical process monitoring schemes. Technometrics. 1991;33:299–313. doi: 10.1080/00401706.1991.10484836
   Web of Science ®Google Scholar
• Gan FF. Joint monitoring of process mean and variance using exponentially weighted moving average control charts. Technometrics. 1995;37:446–453. doi: 10.1080/00401706.1995.10484377
   Web of Science ®Google Scholar
• Amin RW, Wolff H, Besenfelder W, Baxley RJR. EWMA control charts for the smallest and largest observations. J Qual Technol. 1999;31:189–206.
   Web of Science ®Google Scholar
• Xie HContributions to qualimetry [PhD thesis]. Winnipeg, Canada: University of Manitoba; 1999.
   Google Scholar
• Chen G, Cheng SW, Xie H. Monitoring process mean and variability with one EWMA chart. J Qual Technol. 2001;33:223–233.
   Web of Science ®Google Scholar
• Chen G, Cheng SW. Max chart: combining X-bar chart and S chart. Stat Sin. 1998;8:263–271.
   Web of Science ®Google Scholar
• Costa AFB, Rahim MA. Monitoring process mean and variability with one non-central Chi-square chart. J Appl Stat. 2004;31:1171–1183. doi: 10.1080/0266476042000285503
   Web of Science ®Google Scholar
• Zhang L, Chen G. An extended EWMA mean chart. Qual Technol Quant Manage. 2005;2:39–52.
   Google Scholar
• Costa AFB, Rahim MA. A single EWMA chart for monitoring process mean and process variance. Qual Technol Quant Manage. 2006;3:295–305.
   Google Scholar
• Chen G, Cheng SW, Xie H. A new EWMA control chart for monitoring both location and dispersion. Qual Technol Quant Manage. 2004;1:217–231.
   Google Scholar
• Khoo MBC, Teh SY, Wu Z. Monitoring process mean and variability with one double EWMA chart. Commun Stat Theory Methods. 2010;39:3678–3694. doi: 10.1080/03610920903324866
   Web of Science ®Google Scholar
• Teh SY, Khoo MBC, Wu Z. A sum of squares double EWMA chart. Comput Ind Eng. 2011;61:1173–1188. doi: 10.1016/j.cie.2011.07.007
   Web of Science ®Google Scholar
• Duncan AJ. The economic design of charts used to maintain current control of a process. J Am Stat Assoc. 1956;51:228–242.
   Web of Science ®Google Scholar
• Montgomery DC. The economic design of control charts: a review and literature survey. J Qual Technol. 1980;12:75–87.
   Web of Science ®Google Scholar
• Ho C, Case K. Economic design of control charts: a literature review for 1981–1991. J Qual Technol. 1994;26:39–53.
   Web of Science ®Google Scholar
• Ho C, Case K. The economically-based EWMA control chart. Int J Prod Res. 1994;32:2179–2186. doi: 10.1080/00207549408956956
   Web of Science ®Google Scholar
• Torng CC, Cochran JK, Montgomery DC, Lawrence FP. Implementing statistically constrained economic EWMA control chart. J Qual Technol. 1995;27:257–260.
   Web of Science ®Google Scholar
• Park C, Lee J, Kim Y. Economic design of a variable sampling rate EWMA chart. IIE Trans. 2004;36:387–399. doi: 10.1080/07408170490426116
   Web of Science ®Google Scholar
• Moskowitz H, Plante R, Chun YH. Effect of quality loss functions on the economic design of X-bar process control charts. Eur J Oper Res. 1994;72:333–349. doi: 10.1016/0377-2217(94)90314-X
   Web of Science ®Google Scholar
• Alexander SM, Dillman MD, Usher JS, Damodaran B. Economic design of control charts using the Taguchi loss function. Comput Ind Eng. 1995;28:671–679. doi: 10.1016/0360-8352(94)00219-D
   Web of Science ®Google Scholar
• Chou CY, Cheng JC, Lai WT. Economic design of variable sampling intervals EWMA charts with sampling at fixed times using genetic algorithms. Expert Syst Appl. 2008;34:419–426. doi: 10.1016/j.eswa.2006.09.009
   Web of Science ®Google Scholar
• Chen YK. Economic design of T2 control charts with the VSSI sampling scheme. Qual Quant. 2009;43:109–122. doi: 10.1007/s11135-007-9101-7
   Web of Science ®Google Scholar
• Woodall WH. Weaknesses of the economic design of control charts. Technometrics. 1986;28:408–409. doi: 10.2307/1269000
   Web of Science ®Google Scholar
• Saniga EM. Economic statistical control chart designs with an application to and R charts. Technometrics. 1989;31:313–320.
   Web of Science ®Google Scholar
• Chou CY, Chen CH, Liu HR. Economic-statistical design of charts for non-normal data by considering quality loss. J Appl Stat. 2000;27:939–951. doi: 10.1080/02664760050173274
   Web of Science ®Google Scholar
• Chen H, Pao Y. The joint economic-statistical design of X̄ and R charts for nonnormal data. Qual Reliab Eng Int. 2011;27:269–280. doi: 10.1002/qre.1116
   Web of Science ®Google Scholar
• Faraz A, Saniga E. Economic statistical design of a T2 control chart with double warning lines. Qual Reliab Eng Int. 2011;27:125–139. doi: 10.1002/qre.1095
   Web of Science ®Google Scholar
• Montgomery DC, Torng CC, Cochran JK, Lawrence FP. Statistically constrained economic design of the EWMA control chart. J Qual Technol. 1995;27:250–256.
   Web of Science ®Google Scholar
• Tolley GO, English JR. Economic designs of constrained EWMA and combined EWMA- control schemes. IIE Trans. 2001;33:429–436.
   Web of Science ®Google Scholar
• Serel DA, Moskowitz H. Joint economic design of EWMA control charts for mean and variance. Eur J Oper Res. 2008;184:157–168. doi: 10.1016/j.ejor.2006.09.084
   Web of Science ®Google Scholar
• Quesenberry CP. On properties of Q charts variables. J Qual Technol. 1995;27:184–203.
   Web of Science ®Google Scholar
• Palisade Corporation. Guide to evolver: the genetic algorithm solver for Microsoft Excel. New York: Newfield; 2001.
   Google Scholar