Developing process-yield-based acceptance sampling plans for AR(1) auto-correlated process

References

• Aslam, M., S. Balamurali, and C. H. Jun. 2021. A new multiple dependent state sampling plan based on the process capability index. ‏ Communications in Statistics-Simulation and Computation. 50 (6):1711–27. doi:10.1080/03610918.2019.1588307.
   Web of Science ®Google Scholar
• Balamurali, S., and C. H. Jun. 2006. Repetitive group sampling procedure for variables inspection. Journal of Applied Statistics 33 (3):327–38. doi:10.1080/02664760500446010.
   Web of Science ®Google Scholar
• Balamurali, S., and M. Usha. 2012. Variables quick switching system with double specification limits. International Journal of Reliability, Quality and Safety Engineering 19 (2)1250008-1–1250008-17. doi:10.1142/S0218539312500088.
   Google Scholar
• Balamurali, S., and M. Usha. 2017. Developing and designing of an efficient variables sampling system based on the process capability index. Journal of Statistical Computation and Simulation 87 (7):1401–15. doi:10.1080/00949655.2016.1267735.
   Web of Science ®Google Scholar
• Balamurali, S., and M. Usha. 2019. Determination of an efficient variables sampling system based on the Taguchi process capability index. Journal of the Operational Research Society 70 (3):420–32. doi:10.1080/01605682.2018.1441637.
   Web of Science ®Google Scholar
• Banihashemi, A., M. S. Fallah Nezhad, and A. Amiri. 2021. A new approach in the economic design of acceptance sampling plans based on process yield index and Taguchi loss function. Computers & Industrial Engineering 159:107155. doi:10.1016/j.cie.2021.107155.
   Web of Science ®Google Scholar
• Boyles, R. A. 1994. Process capability with asymmetric tolerances. Communications in Statistics-Simulation and Computation 23 (3):615–35. doi:10.1080/03610919408813190.
   Web of Science ®Google Scholar
• Dodge, H. F. 1967. A dual system of acceptance sampling. Technical Report No.16, The Statistics Center, Rutgers, The State University, New Brunswick, New Jersey.
   Google Scholar
• Govindaraju, K., and S. Ganesalingam. 1998. Zero acceptance number quick switching system for compliance sampling. Journal of Applied Statistics 25 (3):399–407. doi:10.1080/02664769823124.
   Web of Science ®Google Scholar
• Govindaraju, K., and V. Kuralmani. 1992. Modified tables for the selection of QSS–1 quick switching system for a given (AQL, LQL). Communications in Statistics-Simulation and Computation 21 (4):1103–23. doi:10.1080/03610919208813067.
   Web of Science ®Google Scholar
• Hsu, B. M., M. H. Shu, and T. C. Wang. 2020. Variables adjustable multiple dependent state sampling plans with a loss-based capability index. The International Journal of Advanced Manufacturing Technology 107 (5–6):2163–75. doi:10.1007/s00170-020-05137-9.
   Web of Science ®Google Scholar
• Kotz, S., and C. R. Lovelace. 1998. Process capability indices in theory and practice. London: Arnold.‏
   Google Scholar
• Lee, J. C., H. N. Hung, W. L. Pearn, and T. L. Kueng. 2002. On the distribution of the estimated process yield index Spk. Quality and Reliability Engineering International 18 (2):111–16. doi:10.1002/qre.450.
   Web of Science ®Google Scholar
• Lee, A. H., C. W. Wu, and Y. W. Chen. 2016. A modified variables repetitive group sampling plan with the consideration of preceding lots information. Annals of Operations Research 238 (1–2):355–73. doi:10.1007/s10479-015-2064-5.
   Web of Science ®Google Scholar
• Lee, A. H., C. W. Wu, and Z. H. Wang. 2018. The construction of a modified sampling scheme by variables inspection based on the one-sided capability index. Computers & Industrial Engineering 122:87–94. doi:10.1016/j.cie.2018.05.037.
   Web of Science ®Google Scholar
• Liu, S. W., and C. W. Wu. 2016. A quick switching sampling system by variables for controlling lot fraction nonconforming. International Journal of Production Research 54 (6):1839–49. doi:10.1080/00207543.2015.1084062.
   Web of Science ®Google Scholar
• Lundkvist, P., K. Vännman, and M. Kulahci. 2012. A comparison of decision methods for Cpk when data are autocorrelated. Quality Engineering 24 (4):460–72. doi:10.1080/08982112.2012.710165.
   Web of Science ®Google Scholar
• Mingoti, S. A., and F. L. P. D. Oliveira. 2011. On capability indices for multivariate autocorrelated processes.‏
   Google Scholar
• Montgomery, D. C. 2009. Introduction to Statistical Quality Control. 6th ed. New York: John Wiley & Sons.
   Google Scholar
• Nadi, A. A., B. S. Gildeh, and R. Afshari. 2020. Optimal design of overall yield-based variable repetitive sampling plans for processes with multiple characteristics. Applied Mathematical Modelling 81:194–210. doi:10.1016/j.apm.2019.11.037.
   Web of Science ®Google Scholar
• Negash, Y. T. 2019. Process yield index and variable sampling plans for autocorrelation between nonlinear profiles. IEEE Access. 7:8931–43. doi:10.1109/ACCESS.2018.2889909.
   Web of Science ®Google Scholar
• Nesaee, M., and M. S. Fallah Nezhad. 2021. Designing variables sampling plans based on the yield index Spk. Communications in Statistics-Theory and Methods 50 (3):507–20. doi:10.1080/03610926.2019.1639742.
   Web of Science ®Google Scholar
• Noorossana, R. 2002. Process capability analysis in the presence of autocorrelation. Quality and Reliability Engineering International 18 (1):75–77. doi:10.1002/qre.445.
   Web of Science ®Google Scholar
• Romboski, L. D. 1969. An investigation of quick switching acceptance sampling system. Doctoral Dissertation, Rutgers, The State University, New Brunswick, New Jersey.
   Google Scholar
• Scagliarini, M. 2002. Estimation of Cp for autocorrelated data and measurement errors. Communications in Statistics-Theory and Methods 31 (9):1647–64. doi:10.1081/STA-120013018.
   Web of Science ®Google Scholar
• Sun, J., S. Wang, and Z. Fu. 2010. Process capability analysis and estimation scheme for autocorrelated data. Journal of Systems Science and Systems Engineering 19 (1):105–27. doi:10.1007/s11518-010-5127-9.
   Web of Science ®Google Scholar
• Sun, J., S. Wang, and Z. Fu. 2009. The Effect of Autocorrelated Data on Taguchi Process Capability Index Cpm Based on AR(1) Model. International Conference on Management and Service Science (pp. 1–4). IEEE.‏ doi:10.1109/ICMSS.2009.5301174.
   Google Scholar
• Tamirat, Y., and F. K. Wang. 2019. Acceptance sampling plans based on EWMA yield index for the first order autoregressive process. Journal of the Operational Research Society 70 (7):1179–92. doi:10.1080/01605682.2018.1487819.
   Web of Science ®Google Scholar
• Tamirat, Y., F. K. Wang, and Y. C. Chen. 2017. Sampling schemes by variables inspection for the first order autoregressive model between linear profiles. Mathematical Problems in Engineering 2017: Article ID 3189412 (1–11). doi:10.1155/2017/3189412.
   Web of Science ®Google Scholar
• Vannman, K., and M. Kulahci. 2008. A model‐free approach to eliminate autocorrelation when testing for process capability. Quality and Reliability Engineering International 24 (2):213–28. doi:10.1002/qre.887.
   Web of Science ®Google Scholar
• Wang, F. K., and Y. Tamirat. 2015. Lower confidence bound for process-yield index Spk with autocorrelated process data. Quality Technology & Quantitative Management 12 (2):253–67. doi:10.1080/16843703.2015.11673380.
   Web of Science ®Google Scholar
• Wang, Z. H., and C. W. Wu. 2019a. An improved sampling plan by variables inspection with consideration of process yield and quality loss. Journal of Statistical Computation and Simulation 89 (13):2395–409. doi:10.1080/00949655.2019.1619739.
   Web of Science ®Google Scholar
• Wang, Z. H., and C. W. Wu. 2019b. Improved inspection scheme with a loss-based capability index. The International Journal of Advanced Manufacturing Technology 104 (1):1321–31. doi:10.1007/s00170-019-03980-z.
   Web of Science ®Google Scholar
• Wu, C. W., and J. T. Chen. 2021. A modified sampling plan by variables with an adjustable mechanism for lot sentencing. Journal of the Operational Research Society 72 (3):678–87. doi:10.1080/01605682.2019.1657366.
   Web of Science ®Google Scholar
• Wu, C. W., A. H. Lee, and Y. S. Huang. 2021. A variable-type skip-lot sampling plan for products with a unilateral specification limit.‏. International Journal of Production Research 59 (14):4140–56. doi:10.1080/00207543.2020.1757778.
   Web of Science ®Google Scholar
• Wu, C. W., A. H. Lee, S. W. Liu, and M. H. Shih. 2017. Capability-based quick switching sampling system for lot disposition. Applied Mathematical Modelling 52:131–44. doi:10.1016/j.apm.2017.07.050.
   Web of Science ®Google Scholar
• Wu, C. W., and S. W. Liu. 2018. A new lot sentencing approach by variables inspection based on process yield. International Journal of Production Research 56 (12):4087–99. doi:10.1080/00207543.2018.1424365.
   Web of Science ®Google Scholar
• Wu, C. W., S. W. Liu, J. T. Chen, and J. J. Lin. 2019. Design and construction of a variables switch-based sampling system for product acceptance determination. The International Journal of Advanced Manufacturing Technology 101 (9-12):2643–52. doi:10.1007/s00170-018-3147-7.
   Web of Science ®Google Scholar
• Wu, C. W., and Z. H. Wang. 2017. Developing a variables multiple dependent state sampling plan with simultaneous consideration of process yield and quality loss. International Journal of Production Research 55 (8):2351–64. doi:10.1080/00207543.2016.1244360.
   Web of Science ®Google Scholar
• Zhang, N. F. 1998. Estimating process capability indexes for auto-correlated data. Journal of Applied Statistics 25 (4):559–74. doi:10.1080/02664769823025.
   Web of Science ®Google Scholar