Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Chung-Ho Chen This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Jung-Chen Chen1

1Institute of Industrial Management, Southern Taiwan University, Yung-Kang, Taiwan 710, R.O.C.


 

Received: September 6, 2007
Accepted: June 27, 2008
Publication Date: December 1, 2008

Download Citation: ||https://doi.org/10.6180/jase.2008.11.4.10  


ABSTRACT


Recently, Bowling et al. presented the problem of setting the optimum process mean for a multi-stage serial production system. Their model is based on the maximum of the expected profit per item for determining the optimum process mean. However, they did not take into account the quality cost for the work-in-process and the finished product within the specification limits. In fact, the quality characteristic of the former has a major effect on that of the latter. Hence, the quality characteristics between the work-in-process product and the finished product are dependent. In this paper, we propose a modified Bowling et al.’s model by considering the quality cost for the work-in-process and finished product based on the bivariate quality loss function. The nominal-is-best bivariate quality loss function is applied in evaluating the product quality and formulating the modified model. Finally, the sensitivity analyses of parameters are provided for illustration.


Keywords: Multi-Stage Serial Production System, Process Mean, Bivariate Quality Loss Function


REFERENCES


  1. [1] Carlsson, O., “Determining the Most Profitable Process Level for a Production Process under Different Sales Conditions,” Journal of Quality Technology, Vol. 16, pp. 4449 (1984).
  2. [2] Bisgaard, S., Hunter, W. G. and Pallesen, L., “Economic Selection of Quality of Manufactured Product,” Technometrics, Vol. 26, pp. 918 (1984).
  3. [3] Golhar, D. Y., “Determination of the Best Mean Contents for a ‘Canning Problem’,” Journal of Quality Technology, Vol. 19, pp. 8284 (1987).
  4. [4] Golhar, D. Y., “Computation of the Optimal Process Mean and the Upper Limit for a Canning Problem,” Journal of Quality Technology, Vol. 20, pp.193195 (1988).
  5. [5] Golhar, D. Y. and Pollock, S. M., “Determination of the Optimal Process Mean and the Upper Limit of the Canning Problem,” Journal of Quality Technology, Vol. 20, pp. 188192 (1988).
  6. [6] Golhar, D. Y. and Pollock, S. M., “Cost Savings due to Variance Reduction in a Canning Process,” IIE Transactions, Vol. 24, pp. 8892 (1992).
  7. [7] Cho, B.-R. and Leonard, M. S., “Identification and Extensions of Quasiconvex Quality Loss Functions,” International Journal of Reliability, Quality and Safety Engineering, Vol. 4, pp. 191204 (1997).
  8. [8] Lee, M. K. and Jang, J. S., “The Optimum Target Values for a Production Process with Three-class Screening,” International Journal of Production Economics, Vol. 49, pp. 9199 (1997).
  9. [9] Misiorek, V. I. and Barrnett, N. S., “Mean Selection for Filling Processes under Weights and Measures Requirements,” Journal of Quality Technology, Vol. 32, pp. 111121 (2000).
  10. [10] Phillips, M. D. and Cho, B.-R., “A Nonlinear Model for Determining the Most Economic Process Mean under a Beta Distribution,” International Journal of Reliability, Quality and Safety Engineering, Vol. 7, pp. 6174 (2000).
  11. [11] Lee, M. K. and Elsayed, E. A., “Process Mean and Screening Limits for Filling Processes under TwoStage Screening Procedure,” European Journal of Operational Research, Vol. 138, pp. 118126 (2002).
  12. [12] Lee, M. K., Hong, S. H., Kwon, H. M. and Kim, S. B., “Optimum Process Mean and Screening Limits for a Production Process with Three-class Screening,” International Journal of Reliability, Quality and Safety Engineering, Vol. 7, pp. 179190 (2000).
  13. [13] Lee, M. K., Hong, S. H. and Elsayed, E. A., “The Optimum Target Value under Single and Two-stage Screenings,” Journal of Quality Technology, Vol. 33, pp. 506514 (2001).
  14. [14] Duffuaa, S. O. and Siddiqui, “Integrated Process Targeting and Product Uniformity Model for Three-class Screening,” International Journal of Reliability, Quality and Safety Engineering, Vol. 9, pp. 261274 (2002).
  15. [15] Bowling, S. R., Khasawneh, M. T., Kaewkuekool, S. and Cho, B. R., “A Markovian Approach to Determining Optimum Process Target Levels for a Multistage Serial Production System,” European Journal of Operational Research, Vol. 159, pp. 636650 (2004).
  16. [16] Taguchi, G., Introduction to Quality Engineering, Tokyo, Asian Productivity Organization (1986).
  17. [17] Wu, C. and Tang, G. R., “Tolerance Design for Products with Asymmetric Quality Losses,” International Journal of Production Research, Vol. 36, pp. 2529 2541 (1998).
  18. [18] Li, M.-H. C., “Optimal Setting of the Process Mean for Asymmetrical Quadratic Quality Loss Function,” Proceedings of the Chinese Institute of Industrial Engineers Conference, pp. 415419 (1997).
  19. [19] Li, M.-H. C., “Optimal Setting of the Process Mean for an Asymmetrical Truncated Loss Function,” Proceedings of the Chinese Institute of Industrial Engineers Conference, pp. 532537 (1998).
  20. [20] Li, M.-H. C., “Quality Loss Function Based Manufacturing Process Setting Models for Unbalanced Tolerance Design,” International Journal of Advanced Manufacturing Technology, Vol. 16, pp. 3945 (2000).
  21. [21] Li, M.-H. C., “Unbalanced Tolerance Design and Manufacturing Setting with Asymmetrical Linear Loss Function,” International Journal of Advanced Manufacturing Technology, Vol. 20, pp. 334340 (2002).
  22. [22] Li, M.-H. C., “Optimal Process Setting for Unbalanced Tolerance Design with Linear Loss Function,” Journal of the Chinese Institute of Industrial Engineers, Vol. 19, pp. 1722 (2002b).
  23. [23] Li, M.-H. C. and Cherng, H.-S., “Optimal Setting of the Process Mean for Asymmetrical Linear Quality Loss Function,” 1999 Conference on Technology and Applications of Quality Management for Twenty-first Century, pp. 2-62-11 (1999).
  24. [24] Li, M.-H. C. and Cherng, H.-S., “Unbalanced Tolerance Design with Asymmetric Truncated Linear Loss function,” The 14th Asia Quality Symposium, pp. 162 165 (2000).
  25. [25] Maghsoodloo, S. and Li, M.-H. C., “Optimal Asymmetrical Tolerance Design,” IIE Transactions, Vol. 32, pp. 11271137 (2000).
  26. [26] Li, M.-H. C. and Chou, C.-Y., “Target Selection for an Indirectly Measurable Quality Characteristic in Unbalanced Tolerance Design,” International Journal of Advanced Manufacturing Technology, Vol. 17, pp. 516522 (2001).
  27. [27] Li, M.-H. C. and Wu, F.-W., “A General Model of Unbalanced Tolerance Design and Manufacturing Setting with Asymmetric Quadratic Loss Function,” Proceeding of Conference of the Chinese Society for Quality, pp. 403409 (2001).
  28. [28] Rahim, M. A. and Tuffaha, F., “Integrated Model for Determining the Optimal Initial Settings of the Process Mean and Optimal Production Run Assuming Quadratic Loss Functions,” International Journal of Production Research, Vol. 42, pp. 32813300 (2004).
  29. [29] Chan, W. M. and Ibrahim, R. N., “Evaluating the Quality Level of a Product with Multiple Quality Characteristics,” International Journal of Advanced Manufacturing Technology, Vol. 24, pp. 738742 (2004).
  30. [30] Chan, W. M., Ibrahim, R. N. and Lochert, P. B., “Quality Evaluation Model Using Loss Function for Multiple S-type Quality Characteristics,” International Journal of Advanced Manufacturing Technology, Vol. 26, pp. 98101 (2005a).
  31. [31] Chan, W. M., Ibrahim, R. N. and Lochert, P. B., “Evaluating the Product Quality Level under Multiple Ltype Quality Characteristics,” International Journal of Advanced Manufacturing Technology, Vol. 27, pp. 9095 (2005b).
  32. [32] Teeravaprug, J., “Determining Optimal Process Mean of Two-market Products,” International Journal of Advanced Manufacturing Technology, Vol. 25, pp. 12481253 (2005).