A New Adaptive Genetic Algorithm for Job-Shop Scheduling

Abstract:

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In order to minimize makespan for job-shop scheduling problem (JSP), an improved adaptive genetic algorithm (IAGA) based on hormone modulation mechanism is proposed. This algorithm has characteristics with avoiding overcoming premature phenomenon and slow evolution. The proposed IAGA algorithm is applied to dynamic job-shop scheduling problem (DJSP) and the satisfied result is obtained. By employing the proposed IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently. Therefore it embodies good adaptation to the DJSP (rush order, machine malfunction, and so on).

Info:

Periodical:

Materials Science Forum (Volumes 626-627)

Edited by:

Dongming Guo, Jun Wang, Zhenyuan Jia, Renke Kang, Hang Gao, and Xuyue Wang

Pages:

771-776

DOI:

10.4028/www.scientific.net/MSF.626-627.771

Citation:

L. Wang et al., "A New Adaptive Genetic Algorithm for Job-Shop Scheduling", Materials Science Forum, Vols. 626-627, pp. 771-776, 2009

Online since:

August 2009

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Price:

$35.00

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