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Modified genetic algorithm for flexible job-shop scheduling problems
Author(s)
Teekeng, Wannaporn
Date Issued
January 1, 2012
Type
Conference Paper
Abstract
This paper proposes a modified version of the genetic algorithm for flexible job-shop scheduling problems (FJSP). The genetic algorithm (GA), a class of stochastic search algorithms, is very effective at finding optimal solutions to a wide variety of problems. The proposed modified GA consists of 1) an effective selection method called "fuzzy roulette wheel selection," 2) a new crossover operator that uses a hierarchical clustering concept to cluster the population in each generation, and 3) a new mutation operator that helps in maintaining population diversity and overcoming premature convergence. The objective of this research is to find a schedule that minimizes the makespan of the FJSP. The experimental results on 10 well-known benchmark instances show that the proposed algorithm is quite efficient in solving flexible job-shop scheduling problems. © 2012 Published by Elsevier B.V.
Citation
Procedia Computer Science, 12, 122-128, 2012