A New Genetic Algorithms Combined with Learning Strategy for Flexible Job-Shop Scheduling Problem

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 117)

Abstract

In this paper, we have proposed a new method based on genetic algorithms and the learning by partial injection of sequences for solving the Flexible Job-shop Scheduling Problem (FJSP). Computational experiments show that the AGAIS (II) algorithm outperforms the performance of the AGAIS (I). In fact, the AGAIS (II) gives better solutions than AGAIS (I) in a reasonable computation time.

Keywords

Job-Shop Scheduling Genetic Algorithms Learning Strategy 

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.College of Mechanical EngineeringHebei Polytechnic UniversityTangshanChina

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