Improvement in the performance of production control systems is so important that many of past studies were dedicated to this problem. The applicability of fuzzy controllers in production control systems has been shown in literature. Furthermore, genetic algorithm has been used to optimize the FLCs performance. In this study, the GFLC methodology is used to develop two production control architectures named “genetic distributed fuzzy”, and “genetic supervisory fuzzy” controllers. These control architectures have been applied to single-part-type production systems. In their new application, the GDF and GSF controllers are developed to control multi-part-type and re-entrant production systems. In multi-part-type and re-entrant production systems the priority of production as well as the production rate for each part type is determined by production control systems. The objective function of the GSF controller is to minimize the overall production costs based on work-in-process (WIP) an d backlog cost, while surplus minimization is considered in GDF controller. The results indicate a great improvement in performance of heuristic controllers regarding the production costs.
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Ph.D., Assistant Prof. at Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran. Experienced in Production Planning, Scheduling, and Control Systems, Operations Research, and Artificial Intelligence.Tang S. H., Ph.D., Associate Prof. at Mechanical and Manufacturing Engineering Dept. Universiti Putra Malaysia.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Improvement in the performance of production control systems is so important that many of past studies were dedicated to this problem. The applicability of fuzzy controllers in production control systems has been shown in literature. Furthermore, genetic algorithm has been used to optimize the FLCs performance. In this study, the GFLC methodology is used to develop two production control architectures named ¿genetic distributed fuzzy¿, and ¿genetic supervisory fuzzy¿ controllers. These control architectures have been applied to single-part-type production systems. In their new application, the GDF and GSF controllers are developed to control multi-part-type and re-entrant production systems. In multi-part-type and re-entrant production systems the priority of production as well as the production rate for each part type is determined by production control systems. The objective function of the GSF controller is to minimize the overall production costs based on work-in-process (WIP) an d backlog cost, while surplus minimization is considered in GDF controller. The results indicate a great improvement in performance of heuristic controllers regarding the production costs. 160 pp. Englisch. N° de réf. du vendeur 9783659279119
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Homayouni S. MahdiPh.D., Assistant Prof. at Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran. Experienced in Production Planning, Scheduling, and Control Systems, Operations Research, and Ar. N° de réf. du vendeur 5145254
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Taschenbuch. Etat : Neu. Genetic Fuzzy Controllers for Complex Production Systems | Using Genetic Algorithms to Optimize Fuzzy Logic Controllers | S. Mahdi Homayouni (u. a.) | Taschenbuch | 160 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659279119 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 105948555
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Improvement in the performance of production control systems is so important that many of past studies were dedicated to this problem. The applicability of fuzzy controllers in production control systems has been shown in literature. Furthermore, genetic algorithm has been used to optimize the FLCs performance. In this study, the GFLC methodology is used to develop two production control architectures named ¿genetic distributed fuzzy¿, and ¿genetic supervisory fuzzy¿ controllers. These control architectures have been applied to single-part-type production systems. In their new application, the GDF and GSF controllers are developed to control multi-part-type and re-entrant production systems. In multi-part-type and re-entrant production systems the priority of production as well as the production rate for each part type is determined by production control systems. The objective function of the GSF controller is to minimize the overall production costs based on work-in-process (WIP) an d backlog cost, while surplus minimization is considered in GDF controller. The results indicate a great improvement in performance of heuristic controllers regarding the production costs.Books on Demand GmbH, Überseering 33, 22297 Hamburg 160 pp. Englisch. N° de réf. du vendeur 9783659279119
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Improvement in the performance of production control systems is so important that many of past studies were dedicated to this problem. The applicability of fuzzy controllers in production control systems has been shown in literature. Furthermore, genetic algorithm has been used to optimize the FLCs performance. In this study, the GFLC methodology is used to develop two production control architectures named ¿genetic distributed fuzzy¿, and ¿genetic supervisory fuzzy¿ controllers. These control architectures have been applied to single-part-type production systems. In their new application, the GDF and GSF controllers are developed to control multi-part-type and re-entrant production systems. In multi-part-type and re-entrant production systems the priority of production as well as the production rate for each part type is determined by production control systems. The objective function of the GSF controller is to minimize the overall production costs based on work-in-process (WIP) an d backlog cost, while surplus minimization is considered in GDF controller. The results indicate a great improvement in performance of heuristic controllers regarding the production costs. N° de réf. du vendeur 9783659279119
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