Optimizing Performance of Tablet's Direct Compression Process Using Fuzzy Goal Programming
This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L9 array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs.
[1] Al-Refaie A., "FGP model to optimize performance of tableting process with three quality responses," Transactions of the Institute of Measurement and Control, Vol. 36(3), 2014b, pp. 336–346.
[2] Taguchi, G. "Taguchi Methods. Research and Development," Vol. 1, 1991, (American Suppliers Institute Press, Dearborn, Michigan.
[3] Pignatello, J. J. "Strategies for robust multiresponse quality engineering". IIE Transactions, Vol. 25, 1993, pp. 5–15.
[4] Jean, M. D. and Wang, J. T. "Using a principal component analysis for developing a robust design of electron beam welding". International Journal of Advanced Manufacturing Technology, 28, 2006, pp. 882–889.
[5] Gupta A. Singh H. and Aggarwal A., "Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel," Expert Systems with Applications, Vol. (38), 2011, pp. 6822–6828.
[6] Candan G. and Yazgan H. "Genetic algorithm parameter optimization using Taguchi method for a flexible manufacturing system scheduling problem," International Journal of Production Research, Vol. 53 (3), 2015, pp. 897-915.
[7] Lin H-C. Su C-T. Wang C-C. Chang B-H. and Juang R-C. "Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms," Expert Systems with Applications, Vol. 39(17): 2012, pp. 12918–12925.
[8] Al-Refaie A., M-H Li and K-C. Tai, "Optimizing SUS 304 wire drawing process by grey analysis utilizing Taguchi method," Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material, Vol. 15(6), 2009, pp. 714–722.
[9] Al-Refaie A., "Optimizing SMT performance using comparisons of efficiency between different systems technique in DEA," IEEE Transactions on Electronics Packaging Manufacturing, Vol. 32(4), 2009, pp. 256–264.
[10] Al-Refaie A., "Super-efficiency DEA approach for optimizing multiple quality characteristics in parameter design. International Journal of Artificial Life Research, Vol. 1(2), 2010a, pp. 58–71.
[11] Al-Refaie A., "A Grey-DEA approach for solving the multiresponse problem in Taguchi Method. Journal of Engineering Manufacture," Vol. 224(1), 2010b, pp. 147–158.
[12] Al-Refaie A., "Optimizing correlated QCHs using principal components analysis and DEA techniques. Production Planning & Control, " Vol. 22(07), 2011, pp. 676–689.
[13] Al-Refaie A. and M. Al-Tahat, "Solving the multi-response problem in Taguchi method by benevolent formulation in DEA. Journal of Intelligent Manufacturing, Vol. 22(4), 2011, pp. 505–521.
[14] AL-Refaie A., T. Chen, R. Al-Athamneh, H.C. Wu , " Fuzzy neural network approach to optimizing process performance by using multiple responses," Journal of Ambient Intelligence and Humanized Computing, Vol. 7 (6), 2016, pp. 801-816.
[15] Al-Refaie A. and A. Diabat, "Optimizing convexity defect in a tile industry using fuzzy goal programming," Measurement, Journal of the International Measurement Confederation, Vol. 46 (8), 2013, pp. 2807-2815.
[16] Al-Refaie A., "A proposed satisfaction function model to optimize process performance with multiple quality responses in the Taguchi method. Journal of Engineering Manufacture, 2013. doi: 10.1177/0954405413498583.
[17] Al-Refaie, A., "A proposed weighted additive model to optimize multiple quality responses in the Taguchi method with applications," Journal of Process Mechanical Engineering, Vol. 228(2), 2014a, pp. 291–301.
[18] Al-Refaie, A., Li, M. H., Jarbo, M., Yeh, C. H., & Bata, N., "Imprecise data envelopment analysis model for robust design with multiple fuzzy quality responses," Advances in Production Engineering & Management, Vol. 9(2), 2014a, pp. 83–94.
[19] Al-Refaie A., W. Al-Alaween, A. Diabat, M.H. Li , "Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis," Journal of Intelligent Manufacturing, 2014b. doi:10.1007/s10845-014-0986-4.
[20] Al-Refaie A., "Optimizing multiple quality responses in the Taguchi method using fuzzy goal programming: modeling and applications," International Journal of Intelligent Systems, Vol. 30(6), 2015a, pp. 651–675.
[21] Al-Refaie A., "Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming," Journal of Process Mechanical Engineering, 2015b. doi: 10.1177/0954408915620988.
[1] Al-Refaie A., "FGP model to optimize performance of tableting process with three quality responses," Transactions of the Institute of Measurement and Control, Vol. 36(3), 2014b, pp. 336–346.
[2] Taguchi, G. "Taguchi Methods. Research and Development," Vol. 1, 1991, (American Suppliers Institute Press, Dearborn, Michigan.
[3] Pignatello, J. J. "Strategies for robust multiresponse quality engineering". IIE Transactions, Vol. 25, 1993, pp. 5–15.
[4] Jean, M. D. and Wang, J. T. "Using a principal component analysis for developing a robust design of electron beam welding". International Journal of Advanced Manufacturing Technology, 28, 2006, pp. 882–889.
[5] Gupta A. Singh H. and Aggarwal A., "Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel," Expert Systems with Applications, Vol. (38), 2011, pp. 6822–6828.
[6] Candan G. and Yazgan H. "Genetic algorithm parameter optimization using Taguchi method for a flexible manufacturing system scheduling problem," International Journal of Production Research, Vol. 53 (3), 2015, pp. 897-915.
[7] Lin H-C. Su C-T. Wang C-C. Chang B-H. and Juang R-C. "Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms," Expert Systems with Applications, Vol. 39(17): 2012, pp. 12918–12925.
[8] Al-Refaie A., M-H Li and K-C. Tai, "Optimizing SUS 304 wire drawing process by grey analysis utilizing Taguchi method," Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material, Vol. 15(6), 2009, pp. 714–722.
[9] Al-Refaie A., "Optimizing SMT performance using comparisons of efficiency between different systems technique in DEA," IEEE Transactions on Electronics Packaging Manufacturing, Vol. 32(4), 2009, pp. 256–264.
[10] Al-Refaie A., "Super-efficiency DEA approach for optimizing multiple quality characteristics in parameter design. International Journal of Artificial Life Research, Vol. 1(2), 2010a, pp. 58–71.
[11] Al-Refaie A., "A Grey-DEA approach for solving the multiresponse problem in Taguchi Method. Journal of Engineering Manufacture," Vol. 224(1), 2010b, pp. 147–158.
[12] Al-Refaie A., "Optimizing correlated QCHs using principal components analysis and DEA techniques. Production Planning & Control, " Vol. 22(07), 2011, pp. 676–689.
[13] Al-Refaie A. and M. Al-Tahat, "Solving the multi-response problem in Taguchi method by benevolent formulation in DEA. Journal of Intelligent Manufacturing, Vol. 22(4), 2011, pp. 505–521.
[14] AL-Refaie A., T. Chen, R. Al-Athamneh, H.C. Wu , " Fuzzy neural network approach to optimizing process performance by using multiple responses," Journal of Ambient Intelligence and Humanized Computing, Vol. 7 (6), 2016, pp. 801-816.
[15] Al-Refaie A. and A. Diabat, "Optimizing convexity defect in a tile industry using fuzzy goal programming," Measurement, Journal of the International Measurement Confederation, Vol. 46 (8), 2013, pp. 2807-2815.
[16] Al-Refaie A., "A proposed satisfaction function model to optimize process performance with multiple quality responses in the Taguchi method. Journal of Engineering Manufacture, 2013. doi: 10.1177/0954405413498583.
[17] Al-Refaie, A., "A proposed weighted additive model to optimize multiple quality responses in the Taguchi method with applications," Journal of Process Mechanical Engineering, Vol. 228(2), 2014a, pp. 291–301.
[18] Al-Refaie, A., Li, M. H., Jarbo, M., Yeh, C. H., & Bata, N., "Imprecise data envelopment analysis model for robust design with multiple fuzzy quality responses," Advances in Production Engineering & Management, Vol. 9(2), 2014a, pp. 83–94.
[19] Al-Refaie A., W. Al-Alaween, A. Diabat, M.H. Li , "Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis," Journal of Intelligent Manufacturing, 2014b. doi:10.1007/s10845-014-0986-4.
[20] Al-Refaie A., "Optimizing multiple quality responses in the Taguchi method using fuzzy goal programming: modeling and applications," International Journal of Intelligent Systems, Vol. 30(6), 2015a, pp. 651–675.
[21] Al-Refaie A., "Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming," Journal of Process Mechanical Engineering, 2015b. doi: 10.1177/0954408915620988.
@article{"International Journal of Information, Control and Computer Sciences:75454", author = "Abbas Al-Refaie", title = "Optimizing Performance of Tablet's Direct Compression Process Using Fuzzy Goal Programming", abstract = "This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L9 array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs.
", keywords = "Fuzzy goal programming, control charts, process capability, tablet optimization.", volume = "11", number = "5", pages = "546-6", }
