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Multi-Response Optimization For Industrial Processes
Rahali Elazzouzi Saida, Abdessamad KOBI, Mihaela BARREAU
Pages - 82 - 91 | Revised - 15-08-2013 | Published - 15-09-2013
Published in International Journal of Engineering (IJE)
MORE INFORMATION
KEYWORDS
Multi-Response, Optimization, Discrete, Numerical Modeling.
ABSTRACT
Process optimization is a very important point in modern industry. There are many classical optimization methods, which can be applied when some mathematical conditions are verified. Real situations are not very simple so that classical methods may not succeed in optimizing; as in cases when the optimization has several contradictory objectives (Collette, 2002).
The purpose of this work is to propose an optimization method for industrial processes with multiple inputs and multiple outputs (MIMO), for which the optimization objectives are generally contradictory and for which some objectives are not maximum or minimum but performance criteria.
The first step of this method is modeling each process response by a quadratic model. After establishing the model, we use a simplified numerical optimization algorithm in order to determine values of the parameters allowing optimizing the different responses, for MIMO processes.
This method will also allow finding optimum target values for multiple inputs single output processes.
The purpose of this work is to propose an optimization method for industrial processes with multiple inputs and multiple outputs (MIMO), for which the optimization objectives are generally contradictory and for which some objectives are not maximum or minimum but performance criteria.
The first step of this method is modeling each process response by a quadratic model. After establishing the model, we use a simplified numerical optimization algorithm in order to determine values of the parameters allowing optimizing the different responses, for MIMO processes.
This method will also allow finding optimum target values for multiple inputs single output processes.
Bénabès, J., Bennis, F, Poirson E., & Ravaut, Y. (2010), Interactive optimization strategies for layout problems, International Journal on Interactive Design and Manufacturing,4(3):181–190, 2010. | |
Coello, C. A., An updated survey of ga-based multiobjective optimization techniques. ACM Comput. Surv., 32(2):109–143, 2000. | |
Collette, Y. & Siarry, P. (2002), Optimisation multiobjectif, Eyrolles, 2002. | |
Dean A., & Voss D. (2000), Design and Analysis of Experiments, Springer, 2000. | |
Derringer, G. and R. Suich. Simultaneous optimization of several response variables,Journal | |
E. Harrington, The desirability function, Industrial Quality Control, 21, 494 - 498, 1965. | |
Fowlkes, W.Y. & Creveling, C.M. (1995), Engineering Methods for robust Product Design,Addison-Wesley, 1995. | |
Holland, J. H., Adaptation in Natural and Artificial Systems : An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA,1992. | |
http://www.cours.polymtl.ca/mth6301/mth6301-presentations/2001/Oueslati2001.pdf. | |
Miettinen, K., Nonlinear Multiobjective Optimization, volume 12 de International Series in Operations Research and Management Science. Kluwer Academic Publishers, Dordrecht,1999. | |
Montgomery, D.C. (2001), Design and Analysis of experiments, 5th ed., John Wiley and Sons, 2001. | |
of Quality Technology, vol. 12, 214 - 219, 1980. | |
Oueslati, H. (2001), Optimisation simultanée de plusieurs réponses dans le cas de fabrication, Ecole Polytechnique de Montréal, 2001. | |
Pareto, V., Cours d’´economie politique : professeur à l’Université de Lausanne. Numéro vol. 1. F. Rouge, 1896. | |
T. Gräbener & A. Berro, Optimisation multiobjectif discrète par propagation de contraintes,Actes JFPC, (INRIA-00293720), 2008. | |
Terki, A. (2009), Analyse des performances des algorithmes génétiques utilisant différentes techniques d’évolution de la population, PhD thesis, University Mentouri, Constantine, 2009 | |
Z. He,P. F. Zhu., A Note on Multi-response Robust Parameter Optimization Based on RSM.Management of Innovation and Technology, IEEE International Conference. 1120 - 1123,2008. | |
Zhang, J., Chung, H. S. H., and Zhong, J., Adaptive crossover and mutation in genetic algorithms based on clustering technique, pages 1577–1578, 2005. | |
Professor Rahali Elazzouzi Saida
ENSAT/LABTIC/Abdelmalek ESSAADI University
Tangier, 90000 - Morocco
rahali_elazzouzi@yahoo.fr
Dr. Abdessamad KOBI
ISTIA/LASQUO/Angers University
Angers, 46660 - France
Dr. Mihaela BARREAU
ISTIA/LASQUO/Angers University
Angers, 46660 - France
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