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A neural network and a genetic algorithm for multiobjective scheduling of semiconductor manufacturing plants / O. Baez Senties in Industrial & engineering chemistry research, Vol. 48 N° 21 (Novembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9546–9555 Titre : A neural network and a genetic algorithm for multiobjective scheduling of semiconductor manufacturing plants Type de document : texte imprimé Auteurs : O. Baez Senties, Auteur ; C. Azzaro-Pantel, Auteur ; L. Pibouleau, Auteur Année de publication : 2010 Article en page(s) : pp. 9546–9555 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Artificial  neural  network  technique  Multiobjective  genetic  algorithm  Semiconductor  manufacturing  plants Résumé : Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple objectives to be satisfied simultaneously (maximization of workstation utilization and minimization of waiting time and storage, for instance). In this study, we propose a methodology based on an artificial neural network technique, for computing the various objective functions, embedded into a multiobjective genetic algorithm for multidecision scheduling problems in a semiconductor wafer fabrication environment. A discrete event simulator, developed and validated in our previous works, serves here to feed the neural network. Six criteria related to both equipment (facility average utilization) and products (average cycle time (ACT), standard deviation of ACT, average waiting time, work in process, and total storage) are chosen as significant performance indexes of the workshop. The optimization variables are the time between campaigns and the release time of batches into the plant. An industrial size example is taken as a test bench to validate the approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018577 [article] A neural network and a genetic algorithm for multiobjective scheduling of semiconductor manufacturing plants [texte imprimé] / O. Baez Senties, Auteur ; C. Azzaro-Pantel, Auteur ; L. Pibouleau, Auteur . - 2010 . - pp. 9546–9555. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9546–9555 Mots-clés : Artificial  neural  network  technique  Multiobjective  genetic  algorithm  Semiconductor  manufacturing  plants Résumé : Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple objectives to be satisfied simultaneously (maximization of workstation utilization and minimization of waiting time and storage, for instance). In this study, we propose a methodology based on an artificial neural network technique, for computing the various objective functions, embedded into a multiobjective genetic algorithm for multidecision scheduling problems in a semiconductor wafer fabrication environment. A discrete event simulator, developed and validated in our previous works, serves here to feed the neural network. Six criteria related to both equipment (facility average utilization) and products (average cycle time (ACT), standard deviation of ACT, average waiting time, work in process, and total storage) are chosen as significant performance indexes of the workshop. The optimization variables are the time between campaigns and the release time of batches into the plant. An industrial size example is taken as a test bench to validate the approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018577