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Hybrid Flow-Shop Scheduling Method and Simulation Based on Adaptive Genetic Algorithm

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Abstract:

The n-job, k-stage hybrid flow shop problem is one of the general production scheduling problems. Hybrid flow shop (HFS) problems are NP-Hard when the objective is to minimize the makespan .The research deals with the criterion of makespan minimization for the HFS scheduling problems. In this paper we present a new encoding method so as to guarantee the validity of chromosomes and convenience of calculation and corresponding crossover and mutation operators are designed for optimum sequencing. The simulation results show that the Sequence Adaptive Cross Genetic Algorithm (SACGA) is an effective and efficient method for solving HFS Problems.

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Info:

Periodical:

Applied Mechanics and Materials (Volumes 670-671)

Pages:

1434-1438

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.670-671.1434

Citation:

Cite this paper

Online since:

October 2014

Authors:

Jian Feng Zhao*, Xiao Chun Zhu, Bao Sheng Wang

Keywords:

GA, Hybrid Flow-Shop (HFS), Sequence Adaptive Cross Genetic Algorithm (SACGA), Simulation

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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