Solving the Problem of General Job Shop Problem by Using Improved Cellular Genetic Algorithm

Zi Mu Li; Yi Zhang; Xiao Dong Zheng; Xing Yu Wan

doi:10.4028/www.scientific.net/AMR.945-949.3130

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 945-949Solving the Problem of General Job Shop Problem by...

Solving the Problem of General Job Shop Problem by Using Improved Cellular Genetic Algorithm

Abstract:

An improved cellular genetic algorithm (cGA) is proposed to study the optimization of the job-shop scheduling problem (JSP). Combining with the characteristics of JSP, a sequence-based coding mechanism is presented. The small overlapped neibhborhoods of cGA help to enhance the population diversity and exploration. An adaptive selection operation based on fitness of neighborhood is designed to prevent from getting into local optimal. The improved cellular genetic algorithm is tested on some instances and compared with simple genetic algorithm. The computational results show that the improved cellular genetic algorithm is effective on JSP.

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Periodical:

Advanced Materials Research (Volumes 945-949)

Pages:

3130-3135

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.945-949.3130

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Online since:

June 2014

Authors:

Zi Mu Li, Yi Zhang*, Xiao Dong Zheng, Xing Yu Wan

Keywords:

Adaptive Selection Operator, Cellular Genetic Algorithm, Job-Shop Scheduling Problem, Neighborhood Structure

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* - Corresponding Author

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