Fuzzy genetic scheduling with single and multiple schedulers

Abstract

This paper seeks to determine the feasibility of using fuzzy concepts in conjunction with genetic algorithms to solve multi-objective single machine scheduling considering both single scheduler and multiple schedulers. Another focus of this research is to test the feasibility of gene detection strategies to perform block crossover during the evolution cycle of the genetic algorithm. During block crossover, the good gene segments from the parent chromosomes are preserved in the child chromosomes. Several problems with known optimal solutions for non-zero ready times are tested using the proposed method and the results are reported. Different membership function types and fitness function evaluation methods are also compared. The results indicate that the proposed method finds the optimal solution with high frequency. Finally, the paper also discusses how multiple schedulers with different preferences could find the optimal solution as a group even though individual schedulers cannot always find it.