Baez Senties, Oscar and Azzaro-Pantel, Catherine and Pibouleau, Luc and Domenech, Serge A neural network and a genetic algorithm for multiobjective scheduling of semiconductor manufacturing plants. (2009) Industrial & Engineering Chemistry Research, vol. 48 (n° 11). pp. 9546-9555.
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Official URL: http://dx.doi.org/10.1021/ie8018577
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 multi-decision 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.
|Additional Information:||This publication is available on http://pubs.acs.org/journal/iecred|
|Audience (journal):||International peer-reviewed journal|
|Institution:|| Université de Toulouse > Institut National Polytechnique de Toulouse - INPT|
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
French research institutions > Centre National de la Recherche Scientifique - CNRS
Laboratoire de Génie Chimique - LGC (Toulouse, France) - Procédés Systèmes Industriels (PSI) - Conception Optimisation Ordonnancement des Procédés (COOP)
|Deposited By:||Catherine AZZARO PANTEL|
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