Optimising performance of low-voltage cables’ process with three quality responses using fuzzy goal programming

References

• Taguchi G. Taguchi methods. Research and development. Vol. 1, Dearborn, MI: American Suppliers Institute Press; 1991.
   Google Scholar
• Phadke MS. Quality engineering using robust design. Englewood Cliffs, NJ: Prentice-Hall; 1989.
   Google Scholar
• Khoei AR, Masters I, Gethin DT. Design optimization of aluminium recycling processes using Taguchi technique. Journal of Materials Processing Technology. 2002;127: 96–106. doi: 10.1016/S0924-0136(02)00273-X
   Web of Science ®Google Scholar
• Al-Refaie A, Li M-H, Chang B-C, Jalham I. Application of the Taguchi approach to improve performance of the alignment-layer printing process in liquid crystal displays. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2012;226(7):1241–1248.
   Google Scholar
• Al-Refaie A, Li M-H. Optimizing the performance of plastic injection molding using weighted additive model in goal programming. International Journal of Fuzzy System Applications. 2011;22(07):676–689.
   Google Scholar
• Al-Refaie A. Optimizing SMT performance using comparisons of efficiency between different systems technique in DEA. IEEE Transactions on Electronics Packaging Manufacturing. 2009;32(4):256–264. doi: 10.1109/TEPM.2009.2029238
   Web of Science ®Google Scholar
• Al-Refaie A, Li M-H, Tai K-C. Optimizing SUS 304 wire drawing process by grey analysis utilizing Taguchi method. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material. 2009;15(6): 714–722.
   Web of Science ®Google Scholar
• Al-Refaie A. A Grey-DEA approach for solving the multi-response problem in Taguchi Method. Journal of Engineering Manufacture. 2010;224(1):147–158. doi: 10.1243/09544054JEM1525
   Web of Science ®Google Scholar
• Su CT, Tong LI. Multi-response robust design by principal component analysis. Total Quality Management. 1997;8(6):409–416. doi: 10.1080/0954412979415
   Web of Science ®Google Scholar
• Su CT, Chiang TL. Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach. Journal of Intelligent Manufacturing. 2003;14(2):229–238. doi: 10.1023/A:1022959631926
   Web of Science ®Google Scholar
• Jeyapaul R, Shahabudeen P, Krishnaiah K. Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm. International Journal of Advanced Manufacturing Technology. 2006;30:870–878. doi: 10.1007/s00170-005-0095-9
   Web of Science ®Google Scholar
• Al-Refaie A, Al-Tahat M. Solving the multi-response problem in Taguchi method by benevolent formulation in DEA. Journal of Intelligent Manufacturing. 2011;22(4):505–521. doi: 10.1007/s10845-009-0312-8
   Web of Science ®Google Scholar
• Al-Refaie A. Optimizing correlated QCHs using principal components analysis and DEA techniques. Production Planning & Control. 2011;22(07):676–689. doi: 10.1080/09537287.2010.526652
   Web of Science ®Google Scholar
• Al-Refaie A. Optimizing performance with multiple responses using cross-evaluation and aggressive formulation in DEA. IIE Transactions. 2012;44:1–15. doi: 10.1080/0740817X.2011.566908
   Web of Science ®Google Scholar
• Al-Refaie A, Rawabdeh I, Abu-alhaj R, Jalham I. A fuzzy multiple regression approach for optimizing multiple responses in the Taguchi method. International Journal of Fuzzy System Applications. 2012;2(3):13–34. doi: 10.4018/ijfsa.2012070102
   Google Scholar
• Al-Refaie A, Hamaideen K. Six-sigma management and grey relational analysis to improve performance of tableting process. International Journal of Productivity and Quality Management. 2014;13(2):219–234. doi: 10.1504/IJPQM.2014.059174
   Google Scholar
• Hannan EL. On fuzzy goal programming. Decision Sciences. 1981;12:522–531. doi: 10.1111/j.1540-5915.1981.tb00102.x
   Google Scholar
• Sengupta S. Goal programming approach to a type of quality control problem. The Journal of the Operational Research Society. 1981;32(10):207–211. doi: 10.1057/jors.1981.43
   Web of Science ®Google Scholar
• Martel JM, Aouni B. Diverse imprecise goal programming model formulations. Journal of Global Optimization. 1998;12:127–138. doi: 10.1023/A:1008206226608
   Web of Science ®Google Scholar
• Yaghoobi MA, Tamiz M. A method for solving fuzzy goal programming problems on MINMAX approach. European Journal of Operational Research. 2007;177:1580–1590. doi: 10.1016/j.ejor.2005.10.022
   Web of Science ®Google Scholar
• Cherif MS, Chabchoub H, Aouni B. Quality control system design through the goal programming model and the satisfaction functions. European Journal of Operational Research. 2008;186:1084–1098. doi: 10.1016/j.ejor.2007.04.025
   Web of Science ®Google Scholar
• Yaghoobi MA, Jones DF, Tamiz M. Weighted additive models for solving fuzzy goal programming problems. Asia-Pacific Journal of Operational Research. 2008;25(5):715–733. doi: 10.1142/S0217595908001973
   Web of Science ®Google Scholar
• Al-Refaie A. A proposed weighted additive model to optimize multiple quality responses in the Taguchi method with applications. Journal of Process Mechanical Engineering. 2014;228(2):291–301. doi: 10.1177/0954408913482427
   Web of Science ®Google Scholar
• Montgomery DC. Introduction to statistical quality control. New York: John Wiley & Sons. Inc.; 2009.
   Google Scholar
• Kane VE. Process capability indices. Journal of Quality Technology. 1986;18:41–52.
   Web of Science ®Google Scholar
• Lotz S, Lovelace CR. Process capability indices in theory and practice. London: Arnold; 1998.
   Google Scholar