2009
DOI: 10.1021/ie8018577
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A Neural Network and a Genetic Algorithm for Multiobjective Scheduling of Semiconductor Manufacturing Plants

Abstract: 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 en… Show more

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Cited by 11 publications
(3 citation statements)
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“…The pre-factor 1/2 is not necessary but leads to a compact result [16]. The gradual adjustment in the training finally obtains a good weight distribution [18].…”
Section: Theoretical Fundamentals Of Mlpmentioning
confidence: 99%
“…The pre-factor 1/2 is not necessary but leads to a compact result [16]. The gradual adjustment in the training finally obtains a good weight distribution [18].…”
Section: Theoretical Fundamentals Of Mlpmentioning
confidence: 99%
“…Other authors combine different optimization heuristics, as for example in the work of Ponnambalam and Mohan‐Reddy where SA is combined with GA, where SA serves as a local optimizer. In this context, it is important to note the inclusion of discrete‐event simulation (DES) model to represent dynamically the production system behavior as in Azzaro‐Pantel et al, or the replacement of such DES by an artificial neural network . Other developments include fuzzy logic or probabilistic representations for different variables in the treatment of uncertainty.…”
Section: Introductionmentioning
confidence: 99%
“…Simulated Annealing (SA) has been combined with GA in the work of Ponnambalam and Mohan-Reddy (2003). In this context, it is also noteworthy to mention the use of discrete-event simulation (DES) models to represent dynamically the production system behavior as in (Azzaro-Pantel et al, 1998), or the replacement of such DES by neural networks (ANN) (Senties et al, 2009(Senties et al, , 2010. Other developments include fuzzy logic (Aguilar-Lasserre et al, 2009) or probabilistic (Bonll et al, 2008) representations for uncertainty treatment.…”
Section: Introductionmentioning
confidence: 99%