A New Adaptive Genetic Algorithm for Job-Shop Scheduling

Lei Wang; Dun Bing Tang; W.D. Yuan; M.J. Xu; M. Wan

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

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HomeMaterials Science ForumMaterials Science Forum Vols. 626-627A New Adaptive Genetic Algorithm for Job-Shop...

A New Adaptive Genetic Algorithm for Job-Shop Scheduling

Abstract:

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).

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Advances in Materials Manufacturing Science and Technology XIII Volume I

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Periodical:

Materials Science Forum (Volumes 626-627)

Pages:

771-776

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.626-627.771

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Online since:

August 2009

Authors:

Lei Wang, Dun Bing Tang, W.D. Yuan, M.J. Xu, M. Wan

Keywords:

Dynamic Job-Shop Scheduling Problem (DJSP), Hormone Modulation Mechanism, Improved Adaptive Genetic Algorithm (IAGA), Partheno-Genetic Algorithm (PGA)

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