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Author (up) Gutiérrez, E.; Lozano, S. doi  openurl
  Title Data Envelopment Analysis of multiple response experiments Type
  Year 2010 Publication Applied Mathematical Modelling Abbreviated Journal  
  Volume 34 Issue 5 Pages 1139-1148  
  Keywords Off-line quality control; Robust design; Multiple response; Artificial Neural Networks; Data Envelopment Analysis  
  Abstract Taguchi method is the usual strategy in robust design and involves conducting experiments using orthogonal arrays and estimating the combination of factor levels that optimizes a given performance measure, typically a signal-to-noise ratio. The problem is more complex in the case of multiple responses since the combinations of factor levels that optimize the different responses usually differ. In this paper, an Artificial Neural Network, trained with the experiments results, is used to estimate the responses for all factor level combinations. After that, Data Envelopment Analysis (DEA) is used first to select the efficient (i.e. non-dominated) factor level combinations and then for choosing among them the one which leads to a most robust quality loss penalization. Mean Square Deviations of the quality characteristics are used as DEA inputs. Among the advantages of the proposed approach over traditional Taguchi method are the non-parametric, non-linear way of estimating quality loss measures for unobserved factor combinations and the non-parametric character of the performance evaluation of all the factor combinations. The proposed approach is applied to a number of case studies from the literature and compared with existing approaches.  
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  Call Number Admin @ admin @ GutierrezLozano2010 Serial 4778  
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