期刊
COMPLEX ADAPTIVE SYSTEMS 2012
卷 12, 期 -, 页码 122-128出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2012.09.041
关键词
Flexible Job-Shop Scheduling Problems; Genetic Algorithm; Fuzzy Roulette Wheel Selection; Hierarchical Clustering
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.
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