Abstract
Aiming at the short-run discrete manufacturing process with setup error, the optimal adjustment scheme to minimize the total process quality loss for the situation of adjustment with quadratic cost and considering adjustment error based on autoregressive (AR) model is developed. Based on the state-space process control model, the optimal adjustment scheme is derived by using Kalman filter on line estimation and linear quadratic Gaussian (LQG) theory. A simulation case is presented to illustrate the implement method of the optimal adjustment policy. Furthermore, the optimal adjustment scheme is compared with other quality control policy by simulations, and the results show that the adjustment solution presented by this paper is more effective than other to reduce the total quality loss of the process.
This research is supported by the key project of the National Science Foundation of China (Grant No.70931004,71071107)
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References
Castilio D, Pan R, Colosimo BM (2003) Small sample comparison of some setup adjustment methods. Commun Stat Simul Comput 32(3):923–942
Colosimo BM, Pan R, Castillo ED (2004) A sequential Markov Chain Monte Carlo approach to setup process adjustment over a set of lots. J Appl Stat 31(5):499–520
Colosimo BM, Pan R, Castillo ED (2005) Setup adjustment for discrete-part manufacturing processes with asymmetric cost functions. Int J Prod Res 43(18):3837–3854
Del Castillo E, Pan R, Colosimo BM (2003) A unifying view of some process adjustment methods. J Qual Technol 35(3):286–293
Deming W (1986) Out of crisis. MIT Center for advanced Engineering Study, Cambridge, MA, pp 309–320, ch. 11
Grubbs FE (1983) An optimum procedure for setting machines or adjusting processes. J Qual Technol 15(4):186–189
Lian Z, Castillo ED (2006) Setup adjustment under unknown process parameters and fixed adjustment cost. Stat Plan Inference 136:1039–1060
Lian Z, Colosimo BM, Castillo D (2006a) Setup error adjustment: sensitivity analysis and a new MCMC control rule. Qual Reliab Eng Int 22(4):403–418
Lian Z, Colosimo BM, Castillo ED (2006b) Setup adjustment of multiple lots using a sequential Monte Carlo method. Technometrics 48(3):373–385
Liu LP, Ma YZ (2010) Deadband adjustment scheme for multivariate process. Sys Eng Theory Pract 30(3):538–542 16 (Chinese)
Liu LP, Ma YZ (2011) Research on setup adjustment problem considering random adjustment error. Appl Stat Manag 30(5):896–903 (Chinese)
Pan R, Del Castillo E (2003) Scheduling methods for the setup adjustment problem. Int J Prod Res 41(7):1467–1481. Correction, 2004, 42(1):211–212
Sullo P, Vandevan M (1999) Optimal adjustment strategies for a process with run to run variation and 0–1 quality loss. IIE Trans 31:1135–1145
Trietsch D (1998) The harmonic rule for process setup adjustment with quadratic loss. J Qual Technol 30(1):75–84
Trietsch D (2000) Process setup adjustment with quadratic loss. IIE Trans 32(4):299–307
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Zhang, Zj., Niu, Zw., He, Z., Zhang, Xt. (2013). Research on Setup Adjustment Problem Considering Adjustment Error Based on AR Model. In: Dou, R. (eds) Proceedings of 2012 3rd International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33012-4_28
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DOI: https://doi.org/10.1007/978-3-642-33012-4_28
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