Méthodologie d'aide à la décision multicritère pour l'ordonnancement d'ateliers discontinus

Les ateliers de fabrication de composants électroniques sont caractérisés par un mode opératoire discontinu et flexible, par un flux de produits cyclique et par un fort besoin en équipements qui rend complexe leur gestion. L'objectif des travaux de ce mémoire est l'optimisation multicritère de ces activités de production, donnant lieu à un problème d'ordonnancement à court terme. Le modèle de Simulation à Événements Discrets (SED) habituellement employé est cependant lourdement pénalisé par le temps de calcul nécessaire au traitement de problèmes de taille industrielle. Le SED est ainsi remplacé par une technique de modélisation reposant sur des réseaux de neurones, au sein desquels un algorithme de rétropropagation est mis en oeuvre. Le temps de calcul se trouve alors considérablement réduit. Enfin, lors de la phase d'optimisation, l'utilisation d'un Algorithme Génétique Multicritère (AGM) offre la possibilité de considérer de plusieurs critères d'évaluation. La démarche est validée sur un exemple didactique, représentatif des industries de fabrication de semi-conducteurs. ABSTRACT : Scheduling of electronic components manufacturing systems is identified as a complex task, mainly because of the typical features of the process scheme, such as cyclic flows and the high number of equipment items. Actually, production managers have to cope with various objectives, which contribute also to scheduling complexity. Discrete-event simulation (DES) is one of the most widely used methods to study, analyze, design, and improve manufacturing systems, however their applications in industrial processes takes an enormous computing time. In this study, we propose the DES substitution by an approach based on a neural network technique coupled with a multiobjective genetic algorithm for multi-decision scheduling problems in semiconductor wafer fabrication. The training phase of the neural network was performed by use of the previously developed discrete-event simulator, by using a backpropagation algorithm. The neural networks are then embedded in a multiobjective genetic algorithm (MOGA) to optimize the decision variables and to deal with the set of compromise solutions for the studied criteria, thus giving the optimal Pareto zone solutions. The computing time is then considerably reduced. The program efficiency is validate by means of a simplified industrial examples based on semiconductor manufacturing

Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

Assessment of mono and multi-objective optimization to design a hydrogen supply chain

This work considers the potential future use of hydrogen in fuel cell electrical vehicles to face problems such as global warming, air pollution, energy security and competitiveness. The lack of current infrastructure has been identified as one of the main barriers to develop the hydrogen economy. This work is focused on the design of a hydrogen supply chain through mixed integer linear programming used to find the best solutions for a multiobjective optimization problem in which three objectives are involved, i.e., cost, global warming potential and safety risk. This problem is solved by implementing an 3-constraint method. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making is then recommended to find the best solution through an M-TOPSIS analysis. The model is applied to the Great Britain case study previously treated in the dedicated literature. Mono and multicriteria optimizations exhibit some differences concerning the degree of centralization of the network and the selection of the production technology type

Méthodologie d'aide à la décision multicritère pour l'ordonnancement d'ateliers discontinus

Les ateliers de fabrication de composants électroniques sont caractérisés par un mode opératoire discontinu et flexible, par un flux de produits cyclique et par un fort besoin en équipements qui rend complexe leur gestion. L'objectif des travaux de ce mémoire est l'optimisation multicritère de ces activités de production, donnant lieu à un problème d'ordonnancement à court terme. Le modèle de Simulation à Événements Discrets (SED) habituellement employé est cependant lourdement pénalisé par le temps de calcul nécessaire au traitement de problèmes de taille industrielle. Le SED est ainsi remplacé par une technique de modélisation reposant sur des réseaux de neurones, au sein desquels un algorithme de rétropropagation est mis en oeuvre. Le temps de calcul se trouve alors considérablement réduit. Enfin, lors de la phase d'optimisation, l'utilisation d'un Algorithme Génétique Multicritère (AGM) offre la possibilité de considérer de plusieurs critères d'évaluation. La démarche est validée sur un exemple didactique, représentatif des industries de fabrication de semi-conducteursScheduling of electronic components manufacturing systems is identified as a complex task, mainly because of the typical features of the process scheme, such as cyclic flows and the high number of equipment items. Actually, production managers have to cope with various objectives, which contribute also to scheduling complexity. Discrete-event simulation (DES) is one of the most widely used methods to study, analyze, design, and improve manufacturing systems, however their applications in industrial processes takes an enormous computing time. In this study, we propose the DES substitution by an approach based on a neural network technique coupled with a multiobjective genetic algorithm for multi-decision scheduling problems in semiconductor wafer fabrication. The training phase of the neural network was performed by use of the previously developed discrete-event simulator, by using a backpropagation algorithm. The neural networks are then embedded in a multiobjective genetic algorithm (MOGA) to optimize the decision variables and to deal with the set of compromise solutions for the studied criteria, thus giving the optimal Pareto zone solutions. The computing time is then considerably reduced. The program efficiency is validate by means of a simplified industrial examples based on semiconductor manufacturingTOULOUSE-ENSIACET (315552325) / SudocSudocFranceF

Development of a Multiobjective Scheduler for Semiconductor Manufacturing

Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple and conflicting objectives (meeting due dates and minimizing waiting time for instance) to satisfy. In this study, we propose an effective approach based an artificial neural network technique embedded in a multiobjective optimization loop for multi-decision scheduling problems in a semiconductor wafer fabrication environment