Genetic Fuzzy Controllers for Complex Production Systems
Using Genetic Algorithms to Optimize Fuzzy Logic Controllers
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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,...
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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