Hybrid Taguchi-based Genetic Algorithm for Flowshop Scheduling Problem
نویسندگان
چکیده
A hybrid Taguchi-based genetic algorithm (HTGA) is developed for solving multi-objective flowshop scheduling problems (FSPs). Search performance is improved by using Taguchi-based crossover to avoid scheduling conflicts, and dynamic weights are randomly selected by a fuzzy inference system. The conventional approach to selecting dynamic weights randomly ignores small value for the objective when the weight value is very small. A numerical example is given to demonstrate the application of the proposed hybrid method and its good performance. The numerical results show that the hybrid method effectively enhances the genetic algorithm. The improvement achieved by the HTGA also exceeds that obtained by existing methods reported in the literature for finding Pareto optimum solutions for FSPs. Therefore, the HTGA effectively solves multi-objective flowshop scheduling problems.
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