Research on Improved Genetic Algorithm Solving Flexible Job-Shop Problem

Min Shuo Li; Ming Hai Yao

doi:10.4028/www.scientific.net/AMR.479-481.1918

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 479-481Research on Improved Genetic Algorithm Solving...

Research on Improved Genetic Algorithm Solving Flexible Job-Shop Problem

Abstract:

Based on the analyzing of the characteristic of the flexible job-shop scheduling problem (FJSP), we proposed an improved genetic algorithm. To consider the max finish-time, total delay-time, keeping workload balance among the machines, a new selection operator is proposed, which combines random method, proportion-based selection method with elitist retention policy. The improved genetic algorithm using the proposed selection operator is tested on some standard instances. The experimental results validate the effectiveness of the proposed algorithm.

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Periodical:

Advanced Materials Research (Volumes 479-481)

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1918-1921

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.479-481.1918

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Online since:

February 2012

Authors:

Min Shuo Li, Ming Hai Yao

Keywords:

Flexible Job Shop Scheduling, Genetic Algorithm (GA)

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References

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