TY - JOUR ID - 71067 TI - A Model Based on Neural Network and Data Envelopment Analysis to Optimize Multi-Response Taguchi under Uncertainty JO - Advances in Industrial Engineering JA - AIE LA - en SN - AU - Omrani, Hashem AU - Fouladi, Samira AU - Kheirollahi, Fatemeh AD - Department of Industrial Engineering, Urmia University, Urmia, Iran Y1 - 2018 PY - 2018 VL - 52 IS - 2 SP - 223 EP - 232 KW - DEA KW - Design of Experiments KW - Grey Numbers KW - neural network KW - Taguchi Method DO - 10.22059/jieng.2018.138503.1041 N2 - “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. UR - https://aie.ut.ac.ir/article_71067.html L1 - https://aie.ut.ac.ir/article_71067_e9d17bfa4d498687e3f5b63b0c9438d8.pdf ER -