A hybrid Nelder–Mead simplex and PSO approach on economic and economic-statistical designs of MEWMA control charts
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  • 作者:Farnaz Barzinpour (1)
    Rassool Noorossana (1)
    Seyed Taghi Akhavan Niaki (2)
    Mohammad Javad Ershadi (1)
  • 关键词:Control chart ; MEWMA ; Economic design ; Economic ; statistical design ; Particle swarm optimization ; Nelder–Mead simplex search ; Comparative study
  • 刊名:The International Journal of Advanced Manufacturing Technology
  • 出版年:2013
  • 出版时间:12 - April 2013
  • 年:2013
  • 卷:65
  • 期:9
  • 页码:1339-1348
  • 全文大小:270KB
  • 参考文献:1. Duncan AJ (1956) The economic design of x-charts used to maintain current control of a process. J Am Stat Assoc 51:228-42
    2. Lorenzen TJ, Vance LC (1986) The economic design of control charts: a unified approach. Technometrics 28:3-0 CrossRef
    3. Saniga EM (1989) Economic statistical control chart with an application to X-bar and R charts. Technometrics 31:313-20
    4. Park C, Lee J, Kim Y (2004) Economic design of a variable sampling rate EWMA chart. IIE Trans 36:387-99 CrossRef
    5. Chou CY, Cheng JC, Lai WT (2008) Economic design of variable sampling intervals EWMA charts with sampling at fixed times using genetic algorithms. Expert Syst Appl 34:419-26 CrossRef
    6. Tolley GO, English JR (2001) Economic design of constrained EWMA and combined EWMA- x-bar control shemes. IIE Trans 33:429-36
    7. Serel DA, Moskowitz H (2008) Joint economic design of EWMA control charts for mean and variance. Eur J Oper Res 184:157-68 CrossRef
    8. Serel AS (2009) Economic design of EWMA control charts based on loss function. Math Comput Model 49:745-59 CrossRef
    9. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308-13 CrossRef
    10. Linderman K, Love TE (2000) Economic and economic statistical designs for MEWMA control charts. J Qual Technol 32:410-17
    11. Molnau WE, Montgomery DC, Runger GC (2001) Statistically constrained economic design of the multivariate exponentially weighted moving average control chart. Qual Reliab Eng Int 7:39-9 CrossRef
    12. Runger GC, Prabhu SS (1996) A Markov chain model for the multivariate exponentially weighted moving average control chart. J Am Stat Assoc 91:1701-706 CrossRef
    13. Niaki STA, Ershadi MJ, Malaki M (2010) Economic and economic-statistical designs of MEWMA control charts-A hybrid Taguchi loss. Markov chain and genetic algorithm approach. Int J Adv Manuf Technol 48:283-96 CrossRef
    14. Hooke R, Jeeves TA (1961) Direct search solution of numerical and statistical problems. J Assoc Comput Mach 8:212-29 CrossRef
    15. Linderman K (1998) Economic design of multivariate exponentially weighted moving average (MEWMA) control charts. Ph.D. Thesis, Case Western Reserve University
    16. Chou CY, Liu HR (2002) Economic -statistical design of multivariate control charts using quality loss function. Int J Adv Manuf Technol 20:916-24 CrossRef
    17. Chen Y, Cheng Y (2007) Non-normality effects on the economic-statistical design of X-bar charts with Weibull in-control time. Eur J Oper Res 176:986-98 CrossRef
    18. Von Collani E (1986) A simple procedure to determine the economic design of an X-bar control chart. J Qual Technol 18:145-51
    19. Chou CY, Chen CH, Liu HR (2006) Economic design of EWMA charts with variable sampling intervals. Qual Quant 40:879-96 CrossRef
    20. Garcia-Diaz C, Aparisi F (2005) Economic design of EWMA control charts using regions of maximum and minimum ARL. IIE Trans 37:1011-021 CrossRef
    21. Malaki M, Ershadi MJ, Niaki STA (2009) Economic and economic-statistical designs of MEWMA control charts: A new meta-heuristic approach. In Proceedings of the Second International Conference of Iranian Operations Research Society, Mazandaran University, Babolsar, Iran, May 20-2
    22. Niaki STA, Malaki M, Ershadi MJ (2011) A particle swarm optimization approach in economic and economic-statistical designs of MEWMA control charts. Published online in Scientia-Iranica, International Journal of Science and Technology, Transactions E
    23. Niaki STA, Ershadi MJ (2011) A parameter-tuned genetic algorithm for statistically constrained economic design of multivariate CUSUM control charts: a Taguchi loss approach. Published online in International Journal of Systems Sciences
    24. Niaki STA, Ershadi MJ (2012) A hybrid ant colony, Markov chain, and experimental design approach for statistically constrained economic design of MEWMA control charts. Expert Syst Appl 39:3265-275 CrossRef
    25. Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks IV, pp. 1942-948, Perth, Australia
    26. Kennedy J, Eberhart R, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco
    27. Omran M (2005) Particle swarm optimization methods for pattern recognition and image processing. PhD thesis, Department of Computer Science, University of Pretoria, South Africa
    28. Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. Proc Evol Prog 98:591-00
    29. Fan S-K, Zahara E (2007) A hybrid simplex search and particle swarm optimization for unconstrained optimization. Eur J Oper Res 181:527-48 CrossRef
    30. Fan S-K, Liang Y-C, Zahara E (2006) A genetic algorithm and particle swarm optimizer hybridized with Nelder–Mead simplex search. Comput Ind Eng 50:401-25 CrossRef
  • 作者单位:Farnaz Barzinpour (1)
    Rassool Noorossana (1)
    Seyed Taghi Akhavan Niaki (2)
    Mohammad Javad Ershadi (1)

    1. Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran, Republic of Islamic
    2. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran, Republic of Islamic
  • ISSN:1433-3015
文摘
Economic design of a control chart involves determining its basic parameters such that a cost function is minimized. This design when statistical performance measures are also considered is referred to as the economic-statistical design. In this paper, a simplex-based Nelder–Mead algorithm is used in combination with a particle swarm meta-heuristic procedure to solve both the economic and economic-statistical designs of a MEWMA control chart. The application results on extensive simulation experiments show that the particle swarm can lead the Nelder–Mead algorithm to better results. Furthermore, a comparative study is performed on the performances of three different algorithms of the Nelder–Mead, the particle swarm optimization (PSO), and the hybrid PSO and Nelder–Mead (PSO–NM). In this study, five different performance measures are taken into consideration and the results for both the economic and the economic-statistical models are reported at the end.
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