Fuzzy logic control of aggregate production planning using genetic algorithms

Abstract

In general, a successful application of fuzzy logic control depends on fuzzy rules, the structure of the rules, and membership functions. This research presents a fuzzy logic control (FLC) model for aggregation production planning by using a genetic algorithm (GA) to simultaneously search the membership functions and inference rule set. It is shown that the genetic algorithm enables us to generate an optimal combination of the membership functions and the rule set. The approach presented in this research is illustrated by using Holt's paint factory data. The comparison of our results with those of Rink and Turksen shows that the proposed approach is the more favorable one. Significance: This research presents a fuzzy logic control (FLC) model for aggregation production planning by using a genetic algorithm (GA) to simultaneously search the membership functions and inference rule set.