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Omrani, Hashem, Fouladi, Samira, Kheirollahi, Fatemeh. (1397). A Model Based on Neural Network and Data Envelopment Analysis to Optimize Multi-Response Taguchi under Uncertainty. , 52(2), 223-232. doi: 10.22059/jieng.2018.138503.1041
Hashem Omrani; Samira Fouladi; Fatemeh Kheirollahi. "A Model Based on Neural Network and Data Envelopment Analysis to Optimize Multi-Response Taguchi under Uncertainty". , 52, 2, 1397, 223-232. doi: 10.22059/jieng.2018.138503.1041
Omrani, Hashem, Fouladi, Samira, Kheirollahi, Fatemeh. (1397). 'A Model Based on Neural Network and Data Envelopment Analysis to Optimize Multi-Response Taguchi under Uncertainty', , 52(2), pp. 223-232. doi: 10.22059/jieng.2018.138503.1041
Omrani, Hashem, Fouladi, Samira, Kheirollahi, Fatemeh. A Model Based on Neural Network and Data Envelopment Analysis to Optimize Multi-Response Taguchi under Uncertainty. , 1397; 52(2): 223-232. doi: 10.22059/jieng.2018.138503.1041

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

Advances in Industrial Engineering
مقاله 8، دوره 52، شماره 2، مهر 2018، صفحه 223-232    اصل مقاله (905.44 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2018.138503.1041
نویسندگان
Hashem Omrani* ؛ Samira Fouladi؛ Fatemeh Kheirollahi
Department of Industrial Engineering, Urmia University, Urmia, Iran
چکیده
“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.
کلیدواژه‌ها
DEA؛ Design of Experiments؛ Grey Numbers؛ neural network؛ Taguchi Method
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