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An Effective Genetic Algorithm for FJSP

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Effective Methods for Integrated Process Planning and Scheduling

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 2))

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Abstract

In this chapter, we proposed an effective genetic algorithm for solving the Flexible Job shop Scheduling Problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate a high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operators are adopted. Various benchmark data taken from the literature are tested. Computational results prove the proposed genetic algorithm is effective and efficient for solving flexible Job shop scheduling problem.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Xinyu Li

  2. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Liang Gao

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  1. Xinyu Li
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  2. Liang Gao
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Correspondence to Liang Gao .

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Li, X., Gao, L. (2020). An Effective Genetic Algorithm for FJSP. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_7

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  • DOI: https://doi.org/10.1007/978-3-662-55305-3_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-55303-9

  • Online ISBN: 978-3-662-55305-3

  • eBook Packages: EngineeringEngineering (R0)

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