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Research Article

Monitoring processes with multiple dependent production lines using time between events control charts

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References

  • Ahmadi Nadi, A., and B. Sadeghpour Gildeh. 2019. A group multiple dependent state sampling plan using truncated life test for the Weibull distribution. Quality Engineering 31 (4):553–63. doi:10.1080/08982112.2018.1558250.
  • Alevizakos, V., C. Koukouvinos, and A. Lappa. 2019. Monitoring of time between events with a double generally weighted moving average control chart. Quality and Reliability Engineering International 35 (2):685–710. doi:10.1002/qre.2430.
  • Alevizakos, V., and C. Koukouvinos. 2020. A progressive mean control chart for monitoring time between events. Quality and Reliability Engineering International 36 (1):161–86. doi:10.1002/qre.2565.
  • Alevizakos, V., K. Chatterjee, and C. Koukouvinos. 2021. A triple exponentially weighted moving average control chart for monitoring time between events. Quality and Reliability Engineering International 37 (3):1059–79. doi:10.1002/qre.2781.
  • Ali, S., A. Pievatolo, and R. Göb. 2016. An overview of control charts for high-quality processes. Quality and Reliability Engineering International 32 (7):2171–89. doi:10.1002/qre.1957.
  • Azadeh, A., M. S. Sangari, and A. S. Amiri. 2012. A particle swarm algorithm for inspection optimization in serial multi-stage processes. Applied Mathematical Modelling 36 (4):1455–64. doi:10.1016/j.apm.2011.09.037.
  • Balan, T. A., and H. Putter. 2019. Nonproportional hazards and unobserved heterogeneity in clustered survival data: When can we tell the difference? Statistics in Medicine 38 (18):3405–20. doi:10.1002/sim.8171.
  • Brook, D., and D. Evans. 1972. An approach to the probability distribution of CUSUM run length. Biometrika 59 (3):539–49. doi:10.1093/biomet/59.3.539.
  • Castagliola, P., G. Celano, D. Rahali, and S. Wu. 2022. Control Charts for Monitoring Time-Between-Events-and-Amplitude Data. In Control Charts and Machine Learning for Anomaly Detection in Manufacturing (43–76. Springer, Cham. doi:10.1007/978-3-030-83819-5_3.
  • Chakraborty, N., S. W. Human, and N. Balakrishnan. 2017. A generally weighted moving average chart for time between events. Communications in Statistics - Simulation and Computation 46 (10):7790–817. doi:10.1080/03610918.2016.1252397.
  • Chakraborty, N., and T. Mahmood. 2021. Failure rate monitoring in generalized gamma-distributed process. Quality Technology & Quantitative Management 18 (6):718–39. doi:10.1080/16843703.2021.1953241.
  • Chan, L. Y., D. K. Lin, M. Xie, and T. N. Goh. 2002. Cumulative probability control charts for geometric and exponential process characteristics. International Journal of Production Research 40 (1):133–50. doi:10.1080/00207540110073073.
  • Chand, S., S. Teyarachakul Prime, and S. Sethi. 2018. Production planning with multiple production lines: Forward algorithm and insights on process design for volume flexibility. Naval Research Logistics (NRL) 65 (6-7):535–49. doi:10.1002/nav.21817.
  • Chen, J. T. 2014. A Shewhart-type control scheme to monitor Weibull data without subgrouping. Quality and Reliability Engineering International 30 (8):1197–214. doi:10.1002/qre.1542.
  • Clayton, D. G. 1978. A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65 (1):141–51. doi:10.1093/biomet/65.1.141.
  • Duan, C., Y. Li, H. Pu, and J. Luo. 2022. Multi-attribute Bayesian fault prediction for hidden-state systems under condition monitoring. Applied Mathematical Modelling 103:388–408. doi:10.1016/j.apm.2021.10.015.
  • Ebrahimipour, V., A. Najjarbashi, and M. Sheikhalishahi. 2015. Multi-objective modeling for preventive maintenance scheduling in a multiple production line. Journal of Intelligent Manufacturing 26 (1):111–22. doi:10.1007/s10845-013-0766-6.
  • Flury, M. I., and M. B. Quaglino. 2018. Multivariate EWMA control chart with highly asymmetric gamma distributions. Quality Technology & Quantitative Management 15 (2):230–52. doi:10.1080/16843703.2016.1208937.
  • Frank, M. J. 1979. On the simultaneous associativity of F(x, y) and x+y−F(x,y). Aequationes mathematicae 19 (1):194–226. doi:10.1007/BF02189866.
  • Genest, C., and B. Rémillard. 2008. Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. Annales de Henri Poincaré 44 (6):1096–127. doi:10.1214/07-AIHP148.
  • Gumbel, E. J. 1960. Distributions des valeurs extremes en plusiers dimensions. Publ. Inst. Statist. Univ. Paris 9:171–3. doi:10.5023/jappstat.32.77.
  • He, Z., Y. Gao, L. Qu, and Z. Wang. 2021. A nonparametric CUSUM scheme for monitoring multivariate time-between-events-and-amplitude data with application to automobile painting. International Journal of Production Research 60 (18):1–18. doi:10.1080/002075.43.2021.1959664.
  • Hu, X., P. Castagliola, J. Zhong, A. Tang, and Y. Qiao. 2021. On the performance of the adaptive EWMA chart for monitoring time between events. Journal of Statistical Computation and Simulation 91 (6):1175–211. doi:10.1080/00949655.2020.1843654.
  • Khoo, M. B., and M. Xie. 2009. A study of time-between-events control chart for the monitoring of regularly maintained systems. Quality and Reliability Engineering International 25 (7):805–19. doi:10.1002/qre.977.
  • Kumar, N., S. Chakraborti, and P. Castagliola. 2022. Phase II exponential charts for monitoring time between events data: Performance analysis using exact conditional average time to signal distribution. Journal of Statistical Computation and Simulation 92 (7):1457–86. doi:10.1080/00949655.2021.1998501.
  • Li, Y., J. Sun, and S. Song. 2012. Statistical analysis of bivariate failure time data with Marshall–Olkin Weibull models. Computational statistics & Data Analysis 56 (6):2041–50. doi:10.1016/j.csda.2011.12.010.
  • Li, S., Y. Xie, M. Farajtabar, A. Verma, and L. Song. 2017. Detecting changes in dynamic events over networks. IEEE Transactions on Signal and Information Processing over Networks 3 (2):346–59. doi:10.1109/TSIPN.2017.2696264.
  • Lu, T. P., and Y. Yih. 2001. An agent-based production control framework for multiple-line collaborative manufacturing. International Journal of Production Research 39 (10):2155–76. doi:10.1080/00207540110038478.
  • Ma, Z., Z. Yang, S. Liu, and S. Wu. 2018. Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. Automation in Construction 95:86–97. doi:10.1016/j.autcon.2018.08.002.
  • Michels, A. S., T. C. Lopes, and L. Magatão. 2020. An exact method with decomposition techniques and combinatorial Benders’ cuts for the type-2 multi-manned assembly line balancing problem. Operations Research Perspectives 7:100163. doi:10.1016/j.orp.2020.100163.
  • Moharib Alsarray, R. M., J. Kazempoor, and A. Ahmadi Nadi. 2021. Monitoring the Weibull shape parameter under progressive censoring in presence of independent competing risks. Journal of Applied Statistics :1–18. doi:10.1080/02664763.2021.2003760.
  • Montgomery, D. C. 2020. Introduction to statistical quality control. New York: John Wiley & Sons.
  • Nadi, A. A., and B. S. Gildeh. 2016. Estimating the lifetime performance index of products for two-parameter exponential distribution with the progressive first-failure censored sample. International Journal for Quality Research 10 (2):389. doi:10.18421/IJQR10.02-10.
  • Nelson, W. B. 2003. Applied life data analysis (Vol. 521). Hoboken, New Jersey: John Wiley & Sons.
  • Nelsen, R. B. 2007. An introduction to copulas. New York: Springer Science & Business Media. doi:10.1007/0-387-28678-0.
  • Rahali, D., P. Castagliola, H. Taleb, and M. B. Khoo. 2019. Evaluation of Shewhart time-between-events-and-amplitude control charts for several distributions. Quality Engineering 31 (2):240–54. doi:10.1080/08982112.2018.1479036.
  • Rahali, D., P. Castagliola, H. Taleb, and M. B. C. Khoo. 2021. Evaluation of Shewhart time-between-events-and-amplitude control charts for correlated data. Quality and Reliability Engineering International 37 (1):219–41. doi:10.1002/qre.2731.
  • Sanusi, R. A., S. Y. Teh, and M. B. Khoo. 2020. Simultaneous monitoring of magnitude and time-between-events data with a Max-EWMA control chart. Computers & Industrial Engineering 142:106378. doi:10.1016/j.cie.2020.106378.
  • Schuh, A., J. A. Camelio, and W. H. Woodall. 2014. Control charts for accident frequency: A motivation for real-time occupational safety monitoring. International journal of Injury Control and Safety Promotion 21 (2):154–62. doi:10.1080/17457300.2013.792285.
  • Shah, M. T., M. Azam, M. Aslam, and U. Sherazi. 2021. Time between events control charts for gamma distribution. Quality and Reliability Engineering International 37 (2):785–803. doi:10.1002/qre.2763.
  • Sparks, R., B. Jin, S. Karimi, C. Paris, and C. R. MacIntyre. 2019. Real-time monitoring of events applied to syndromic surveillance. Quality Engineering 31 (1):73–90. doi:10.1080/08982112.2018.1537443.
  • Talib, A., S. Ali, and I. Shah. 2022. Max-EWMA chart for time and magnitude monitoring using exponentially modified Gaussian distribution. Quality and Reliability Engineering International 38 (2):1092–111. doi:10.1002/qre.3037.
  • Therneau, T. 2015. A package for survival analysis in S. R package Version 2 (7).
  • Wu, S., P. Castagliola, and G. Celano. 2021. A distribution-free EWMA control chart for monitoring time-between-events-and-amplitude data. Journal of Applied Statistics 48 (3):434–54. doi:10.1080/02664763.2020.1729347.
  • Xia, Z. P., J. Y. Yu, L. D. Cheng, L. F. Liu, and W. M. Wang. 2009. Study on the breaking strength of jute fibres using modified Weibull distribution. Composites Part A: Applied Science and Manufacturing 40 (1):54–9. doi:10.1016/j.compositesa.2008.10.001.
  • Xie, Y., M. Xie, and T. N. Goh. 2011. Two MEWMA charts for Gumbel’s bivariate exponential distribution. Journal of Quality Technology 43 (1):50–65. doi:10.1080/00224065.2011.11917845.
  • Xie, F., P. Castagliola, Y. Qiao, X. Hu, and J. Sun. 2021a. A one-sided exponentially weighted moving average control chart for time between events. Journal of Applied Statistics 37 (1):1–30. doi:10.1080/02664763.2021.1967894.
  • Xie, F., J. Sun, P. Castagliola, X. Hu, and A. Tang. 2021b. A multivariate CUSUM control chart for monitoring Gumbel’s bivariate exponential data. Quality and Reliability Engineering International 37 (1):10–33. doi:10.1002/qre.2717.
  • Xie, F., P. Castagliola, J. Sun, A. Tang, and X. Hu. 2022. A one-sided adaptive truncated exponentially weighted moving average scheme for time between events. Computers & Industrial Engineering 168:108052. doi:10.1016/j.cie.2022.108052.
  • Yang, Z., Z. Ma, and S. Wu. 2016. Optimized flowshop scheduling of multiple production lines for precast production. Automation in Construction 72:321–9. doi:10.1016/j.autcon.2016.08.021.
  • Zhang, Y. L., and G. J. Wang. 2007. A geometric process repair model for a series repairable system with k dissimilar components. Applied Mathematical Modelling 31 (9):1997–2007. doi:10.1016/j.apm.2006.08.021.
  • Zhang, C. W., M. Xie, J. Y. Liu, and T. N. Goh. 2007. A control chart for the Gamma distribution as a model of time between events. International Journal of Production Research 45 (23):5649–66. doi:10.1080/00207540701325082.
  • Zhang, Y., Y. Shang, X. Hu, and A. D. Li. 2022. An improved exponential EWMA chart for monitoring time between events. Quality and Reliability Engineering International 38 (5):2748–68. doi:10.1002/qre.3102.
  • Zwetsloot, I. M., T. Mahmood, and W. H. Woodall. 2021. Multivariate time-between-events monitoring: An overview and some overlooked underlying complexities. Quality Engineering 33 (1):13–25. doi:10.1080/08982112.2020.1788717.

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