A genetic-Fuzzy Procedure for Solving Fuzzy Multiresponses Problem

A genetic-Fuzzy Procedure for Solving Fuzzy Multiresponses Problem

Abstract: This research proposed a procedure that combines genetic algorithm (GA) technique and fuzzy goal programming to optimize process performance in experimental design for fuzzy multiple quality characteristics. Initially, regression models were formulated to relate each replicate of a quality characteristic with the process's controllable factors. The GA technique was then employed to determine the optimal factor settings for each response’s replicate. The GA's optimal results were then deployed to develop a fuzzy regression model to relate fuzzy process settings with each quality characteristic. The fuzzy models were adopted to construct the fuzzy desirability and deviation matrices for all quality characteristics. Finally, three optimization models were developed to determine the lower, middle, and upper bounds of optimal factor settings. Three industrial applications, which were widely examined, were employed to illustrate the proposed procedure. Results revealed that the proposed GA-fuzzy procedure efficiently dealt with uncertainty in multiple quality characteristics and process settings by providing fuzzy optimal factor settings rather than crisp values. Such information can support process engineering in understanding the impact of variations/uncertainty on process and product performance and in deciding proper corrective and preventive actions. Compared to the Taguchi method, grey-Taguchi technique, and artificial neural networks approach, the proposed procedure is found efficient in optimizing process performance for multiple quality characteristics under uncertainty.

___

  • [1] G. Taguchi, “Taguchi Methods. Research and Development. Vol. 1,” Dearborn, MI, American Suppliers Institute Press, 1991.
  • [2] N. M. Mehat and S. Kamaruddin, “Investigating the effects of injection molding parameters on the mechanical properties of recycled plastic parts using the Taguchi method,” Materials and Manufacturing Processes, vol. 26, no. 2, pp. 202–209, 2011.
  • [3] R. Çakıroğlu, A. Acır, "Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method," Measurement, vol. 46, pp. 3525–3531, 2013.
  • [4] B.V. Dharmendra, S. P. Kodali, B. N. Rao, "A simple and reliable Taguchi approach for multi-objective optimization to identify optimal process parameters in nano-powder-mixed electrical discharge machining of INCONEL800 with copper electrode," Heliyon, vol. 5, no. 8, pp. e02326, 2019.
  • [5] https://doi.org/10.1016/j.heliyon.2019.e02326.
  • [6] R. Ramakrishnan and L. Karunamoorthy, " Modeling and multiresponse Optimization of Inconel 718 on machining of CNC WEDM process," Journal of Materials Processing Technology, vol. 207, pp. 343-349, 2008.
  • [7] S. Athreya and Y.D. Venkatesh, "Application of Taguchi method for optimization of process parameters in improving the surface roughness of lathe facing operation," International Refereed Journal of Engineering and Science (IRJES), vol. 1, no. 3, pp. 13-19, 2012.
  • [8] J. Antony, R.B. Anand, M. Kumar and M.K., "Tiwari Multiple response optimization using Taguchi methodology and neuro-fuzzy based model," Journal of Manufacturing Technology Management, vol. 17, pp. 908-925, 2006.
  • [9] H. Singh and P. Kumar," Optimizing multi-machining characteristics through Taguchi’s approach and utility concept," Journal of Manufacturing Technology, vol. 17, no. 2, pp. 255-274, 2006.
  • [10] A. Mishra and A. Gangeleb, "Multi-Objective Optimization in Turning of Cylindrical Bars of AISI 1045 Steel through Taguchi’s Method and Utility concept," International Journal of Sciences: Basic and Applied Research (IJSBAR), vol.12, pp. 28-36, 2013.
  • [11] R. Jeyapaul, P. Shahabudeen and K. Krishnaiah, "Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm," International Journal of Advanced Manufacturing Technology, vol. 30, pp. 870–878, 2006.
  • [12] A.R. Yıldız, N. Öztürk, N. Kaya, and F. Öztürk, "Hybrid multiobjective shape design optimization using Taguchi’s method and genetic algorithm," Struct Multidisc Optim, vol. 34, pp. 317–332, 2006.
  • [13] A. Anju and S. Rajender, "A Paper on Multiple Objective Functions of Genetic Algorithm," International Journal of Computer Applications, vol.119, no.10, pp. 0975 – 8887, 2015.
  • [14] K-T., Chiang. “The optimal process conditions of an injection-molded thermoplastic part with a thin shell feature using grey-fuzzy logic: a case study on machining the PC/ABS cell phone shell” Mater Des., vol. 28, pp. 1851–1860, 2007.
  • [15] A. Aman, S. Hari, K. Pradeep, and S., "Manmohan Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique-A comparative analysis, " Journal of materials processing technology, pp. 373–384, 2008.
  • [16] A. Al-Refaie, "Optimizing SMT performance using comparisons of efficiency between different systems technique in DEA," IEEE Transactions on Electronics Packaging Manufacturing; vol. 32, pp. 256–264, 2009.
  • [17] Y. L. Chen, L. H. Chen, and C. Y. Huang, "Fuzzy goal programming approach to solve the equipment-purchasing problem of an FMC," International Journal of Industrial Engineering: Theory, Applications and Practice, vol.16, no. 4, pp. 270-281, 2009.
  • [18] Y. Kazancoglu, U. Esme, M. Bayramoglu, O.Guven, and S. Oz gun, " Multi-objective optimization of the cutting forces in turning operations using the grey-based Taguchi method," Materials and Technology, vol. 45, pp. 105-110, 2011.
  • [19] S. Lal, S. Kumar, Z. Khan, "Multiresponse optimization of wire electrical discharge machining process parameters for Al7075/Al2O3/SiC hybrid composite using Taguchi-based grey relational analysis," Proc IMechE, Part B: J Engineering Manufacture, vol. 229: pp. 229–237, 2015.
  • [20] D.V. Kumar, P.S. Kumar, B. Kumaragurubharan, T. Senthilkumar," Experimental investigation of process parameters in EDM for INCOLOY600 using Taguchi-GRA," Int. J. Eng. Sci. Comp., vol. 6, pp. 6206-6206, 2016.
  • [21] H-C. Lin, C-T. Su, C-C. Wang, "Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms. Expert Syst Appl., vol. 39: pp. 12918–12925, 2012.
  • [22] G. Xu, Z. Yang, G. Long, "Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization," Int J Adv Manuf Technol, vol. 58, pp. 521–531, 2013.
  • [23] M. Bashiri, H. R.Rezaei, A. F. Geranmayeh, & F. Ghobadi, "A comparison of regression and neural network based for multiple response optimization in a real case study of gasoline production process," Journal of Industrial and Systems Engineering, vol. 8, no. 3, pp. 77-94, 2015.
  • [24] S. Karabulut, "Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method," Measurement, vol. 66, pp. 139–149, 2015.
  • [25] C. Venkatesh, R. Venkatesan, "Optimization of process parameters of hot extrusion of SiC/Al 6061 composite using Taguchi’s technique and upper bound technique," Mater Manuf. Process, vol. 30, pp. 85–92, 2015.
  • [26] A. Al-Refaie, T. Chen, R. Al-Athamneh, "Fuzzy neural network approach to optimizing process performance by using multiple responses," Journal of Ambient Intelligent Humanized Computing, vol.7, pp. 801–816, 2016.
  • [27] A. Al-Refaie, "Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming, " Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 231, no. 4, pp. 888-898, 2017.
  • [28] A. Al-Refaie, G. Bani Domi , R. Abdullah, "A fuzzy goal programming-regression approach to optimize process performance of multiple responses under uncertainty," International Journal of Management Science and Engineering Management, vol. 14, no. 1, pp. 20-32, 2019. doi: 10.1080/17509653.2018.1467802.
  • [29] A. Al-Refaie, N. Lepkova, G. Abbasi, G. Bani Domi, "Optimization of process performance by multiple pentagon fuzzy responses: Case studies of wire-electrical discharge machining and sputtering process," Advances in Production Engineering & Management, vol. 15, no. 3, pp 307–317, 2020.
  • [30] https://doi.org/10.14743/apem2020.3.367
  • [31] U. Lee, S. Park, I. Lee, "Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)," Energy, Vol. 196, pp. 117090, 2020.
  • [32] J. H., "Holland Adaptation in natural and artificial systems," Ann Arbor: University of Michigan Press; 1975
  • [33] D.E. Goldberg, "Genetic Algorithms in Search, Optimization, and Machine Learning," Addison-Wesley Professional: Boston, MA, USA, 1989.
  • [34] X. Wu, C-H. Chu, Y. Wang, W. Yan, "A genetic algorithm for cellular manufacturing design and layout," European J Oper Res., vol. 181, no. 1, pp.156–167, 2007.
  • [35] S. N. Sivanandam and S. N. Deepa, "Introduction to Genetic Algorithms," Springer, (1sted), Berlin, Germany, 2008.
  • [36] C.K.H. Lee, "A review of applications of genetic algorithms in operations management," Eng. Appl. Artif. Intell., vol. 76, pp. 1–12, 2018.
  • [37] S. Katoch, S.S. Chauhan, & V. Kumar, "A review on genetic algorithm: past, present, and future," Multimed Tools Appl., vol. 80, pp. 8091–8126, 2021. https://doi.org/10.1007/s11042-020-10139- 6
  • [38] Z. Liu, A. Liu, C., " Wang and Z. Niu, "Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification", Future Generation Computer Systems, vol. 20, no. 7, pp. 1119–1129, 2004.
  • [39] G. Candan, H. Yazgan, "Genetic algorithm parameter optimization using Taguchi method for a flexible manufacturing system scheduling problem," Int J Prod Res., vol. 53, pp. 897–915, 2015.
  • [40] M. A. Şahman, M. Çunkaş, Ş. İnal, F. İnal, B. Coşkun & U. Taşkiran, "Cost optimization of feed mixes by genetic algorithms. Advances in Engineering Software, vol. 40, no. 10, pp. 965-974, 2009.
  • [41] R. Zhang, S.K. Ong, A.Y. C. Nee, "A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling," Appl Soft Comput vol. 37, pp. 521–532, 2015.
  • [42] Y-B Park, J-S Yoo, H-S Park, "A genetic algorithm for the vendormanaged inventory routing problem with lost sales, " Expert Syst Appl., vol. 53, pp. 149–159, 2016.
  • [43] U. Mehboob, J. Qadir, S. Ali, A. Vasilakos, " Genetic algorithms in wireless networking: techniques, applications, and issues. Soft Comput., vol. 20, pp. 2467–2501, 2016.
  • [44] A. Hiassat, A. Diabat, I. Rahwan, "A genetic algorithm approach for location-inventory-routing problem with perishable products," J Manuf Syst., vol. 42, pp. 93–103, 2017
  • [45] C.-C. Chen, C.-C. Tsao, Y.–C. Lin, C.-Y. Hsu, " Optimization of the sputtering process parameters of GZO films using the Grey–Taguchi method," Ceramics International, Vol. 36, no. 3, pp. 979-988, 2010. https://doi.org/10.1016/j.ceramint.2009.11.019.