TY - JOUR ID - 9801 TI - A Comparison of Regression and Neural Network Based for Multiple Response Optimization in a Real Case Study of Gasoline Production Process JO - Journal of Industrial and Systems Engineering JA - JISE LA - en SN - 1735-8272 AU - Bashiri, M AU - Rezaei, H R AU - Geranmayeh, A Farshbaf AU - Ghobadi, F AD - Department of Industrial Engineering, Shahed University, Tehran, Iran. AD - Department of Industrial Engineering, College of Engineering, University of Tehran, Iran. AD - Department of Chemical Engineering, Isfahan University of Technology, Isfahan ,Iran. Y1 - 2015 PY - 2015 VL - 8 IS - 3 SP - 77 EP - 94 KW - multi-response optimization KW - Taguchi method KW - Artificial Neural Network KW - Genetic algorithm KW - fuzzy programming DO - N2 - Most of existing researches for multi response optimization are based on regression analysis. However, the artificial neural network can be applied for the problem. In this paper, two approaches are proposed by consideration of both methods. In the first approach, regression model of the controllable factors and S/N ratio of each response has been achieved, then a fuzzy programming has been applied to find the optimal factors' levels. In the second approach, a tuned Artificial Neural Network (ANN) is used to relate controllable factors and overall exponential desirability function then Genetic Algorithm(GA) is used to find factors optimum value. Mentioned approaches have been discussed in a real case study of oil refining industry. Experimental results for the suggested levels confirm efficiency of the both proposed methods; however, the Neural Network based approach shows more suitability in our case study. UR - https://www.jise.ir/article_9801.html L1 - https://www.jise.ir/article_9801_21b0b0050cc76ca9985dc1c2593eb9ed.pdf ER -