A Model Based on Neural Network and Data Envelopment Analysis to Optimize Multi-Response Taguchi under Uncertainty

Document Type : Research Paper

Authors

Department of Industrial Engineering, Urmia University, Urmia, Iran

Abstract

“Taguchi” is a conventional method for quality control in offline mode. It is used to design and select the best level of parameters for designing a better method to make high quality products. Taguchi method is one-response that is a disadvantage. In the real world, there are several problems with some indicators of quality. Therefore, Taguchi method is not appropriate for optimizing multi-response problems, and we need an engineering and optimizing method to establish the best combination of parameters. On the other hand, due to some uncontrollable factors or the impossibility of empirical conditions, only some of experiments are implemented and a large number of them are incomplete. In this paper, to simulate the remaining experiments the Back-Propagation neural network is used. To overcome one-response problem in Taguchi method, the data envelopment analysis (DEA) is used. Since the results obtained from the neural network are uncertain, DEA model with interval grey data is used. To implement this approach and to identify effective factors, the wear characteristics of composite material PBT, the combined approach based on Taguchi method, neural network and DEA are used.

Keywords

Main Subjects

References

  1. Charnes, A., Cooper, W. W., and Rhodes, E. (1979). “Measuring the Efficiency of Decision Making Units”, European Journal of Operational Research, Vol. 2, No. 6, PP. 429-444.
  2. Fung, C. P., and Kang, P. C. (2005). “Multi-Response Optimization in Friction Properties of PBT Composites Using Taguchi Method and Principle Component Analysis”, Journal of Materials Processing Technology, Vol. 170, No. 3, PP. 602-610.
  3. Taguchi, G. (1978). “Off-Line and on-Line Quality Control Systems”, Proceeding of International Conference on Quality Control. Tokyo, Japan.
  4. Hsieh, K. L., and Tong, L. I. (2001). “Optimization of Multiple Quality Responses Involving Qualitative and Quantitative Characteristics in IC Manufacturing Using Neural Networks”, Computers in Industry, Vol. 46, No. 1, PP. 1-12.
  5. Karabulut, Ş. (2015). Optimization of Surface Roughness and Cutting Force During AA7039/Al2O3 Metal Matrix Composites Milling Using Neural Networks and Taguchi Method. Measurement, 66, PP. 139-149.
  6. Antony, J. (2001). “Simultaneous Optimisation of Multiple Quality Characteristics in Manufacturing Processes Using Taguchi's Quality Loss Function”, The International Journal of Advanced Manufacturing Technology, Vol. 17, No. 2, PP. 134-138.
  7. Reddy, P. B. S., Nishina, K., and Babu, A. S. (1997). “Unification of Robust Design And Goal Programming for Multiresponse Optimization—A Case Study”, Quality and Reliability Engineering International, Vol. 13, No. 6, PP. 371-383.
  8. Su, C. T., and Tong, L. I. (1997). “Multi-Response Robust Design by Principal Component Analysis”, Total Quality Management, Vol. 8, No. 6, PP. 409-416.
  9. Tarng, Y. S., Yang, W. H., and Juang, S. C. (2000). “The Use of Fuzzy Logic in the Taguchi Method for the Optimisation of the Submerged Arc Welding Process”, The International Journal of Advanced Manufacturing Technology, Vol. 16, No. 9, PP. 688-694.­

10.Caporaletti, L. E., Dulá, J. H., and Womer, N. K. (1999). “Performance Evaluation Based on Multiple Attributes With Nonparametric Frontiers”, Omega, Vol. 27, No. 6, PP. 637-645.

11.Liao, H. C., and Chen, Y. K. (2002). “Optimizing Multi-Response Problem in the Taguchi Method by DEA Based Ranking Method”, International Journal of Quality and Reliability Management, Vol. 19, No. 7, PP. 825-837.

12.Liao, H. C. (2004). “A Data Envelopment Analysis Method for Optimizing Multi-Response Problem with Censored Data in the Taguchi Method”, Computers and Industrial Engineering, Vol. 46, No. 4, PP. 817-835.

13.Gutiérrez, E., and Lozano, S. (2010). “Data Envelopment Analysis of Multiple Response Experiments”, Applied Mathematical Modelling, Vol. 34, No. 5, PP. 1139-1148.

14.Ajali, M., and Safari, H. (2011). “Performance Evaluation of Decision Making Units Using the Combined Model of Neural Network Predictive Performance and Data Envelopment Analysis (Case Study: National Iranian Gas Company)”, Journal of Industrial Engineering, Vol. 45, No. 1, PP. 13-29.

15.Rezaiean, J., and Asgari Nezhad, A. (2014). “Performance Evaluation of Water and Sewage Companies in Mazandaran Province by Using the Model of Data Envelopment Analysis and Artificial Neural Networks”, Journal of Industrial Engineering, Vol. 48, No. 2, PP. 201-213.

16.Bashiri, M., Kazemzadeh, R.B., Atkinson, A.C., and Karimi, H. (2011). “Metaheuristic Based Multiple Response Process Optimization”, Journal of Industrial Engineering, Vol. 45, No. 3, PP. 13-23.

17.Chen, M. F., and Tzeng, G. H. (2004). “Combining Grey Relation and TOPSIS Concepts for Selecting an Expatriate Host Country”, Mathematical and Computer Modelling, Vol. 40, No. 13, PP. 1473-1490.

18.Kuo, Y., Yang, T., and Huang, G. W. (2008). “The Use of Grey Relational Analysis in Solving Multiple Attribute Decision-Making Problems”, Computers and Industrial Engineering, Vol. 55, No. 1, PP. 80-93.

19.Huang, Y. P., and Yang, H. P. (2004). “Using Hybrid Grey Model to Achieve Revenue Assurance of Telecommunication Companies”, Journal of Grey System, Vol. 7, No. 1, PP. 38-49.

20.Montgomery, D. C. (2013). Introduction to Statistical Quality Control. Hoboken, NJ: Wiley.

21.Menhaj, M. B. (2013). Fundamentals of Neural Networks. Amir Kabir University of Technology Publication.

22.Ng, D. K. (1994). “Grey System and Grey Relational Model”, ACM SIGICE Bulletin, Vol. 20, No. 2, PP. 2-9.

23.Liu, S., and Lin, Y. (2006). “Grey Information: Theory and Practical Applications”, Springer Science and Business Media.

24.Li, G. D., Yamaguchi, D., and Nagai, M. (2007). “A Grey-Based Decision-Making Approach to the Supplier Selection Problem”, Mathematical and Computer Modelling, Vol. 46, No. 3, PP. 573-581.

25.Sengupta, J. K. (2000). “Efficiency Analysis by Stochastic Data Envelopment Analysis”, Applied Economics Letters, Vol. 7, No. 6, PP. 379-383.

26.Yang, Y. S., Li, L., and Gao, H. L. (1993). “DEA Model for Grey Systems and Its Application”, In Proceedings Intern. AMSE Conference Modeling, Simulation and Control (USTC Press, Hefei, China, 1993) (PP. 1577-1587).

27.Huang, G. H., Baetz, B. W., and Patry, G. G. (1994). “Grey Dynamic Programming for Waste‐Management Planning Under Uncertainty”, Journal of Urban Planning and Development, Vol. 120, No. 3, PP. 132-156.

Advances in Industrial Engineering
Volume 52, Issue 2
July 2018
Pages 223-232

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