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Flexible job shop scheduling based on improved hybrid immune algorithm

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Abstract

An improved hybrid immune algorithm (HIA) with parallelism and adaptability is proposed to solve the flexible job shop scheduling problem. In order to represent the actual characteristics of the problem’s solution, in the algorithm the author uses a hybrid encoding method of piece—machine. Firstly, adaptive crossover operator and mutation operator are designed based on the encoding antibody method and the affinity calculation based on group matching is adopted. Secondly, the algorithm uses adaptive crossover probability and mutation probability in the operation of immune for the antibody population. The new antibody after crossing can automatically meet the constraints of the problem. Next, a hybrid algorithm based on simulated annealing algorithm is introduced to avoid the local optimization in this paper. Finally, it is demonstrated the effectiveness of the proposed algorithm through the simulation and comparison with some existing algorithms.

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Acknowledgments

This work is partially supported by the Talented Young Scholars Growth Plan of Liaoning Province Education Department, China (No. LJQ2013048), the Dr scientific research fund of Liaoning Province (No. 201601244) and the Project of Liaoning BaiQianWan Talents Program, China (No. 2014921062).

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Authors and Affiliations

  1. Institute of Software, Dalian Jiaotong University, 116045, Dalian, China

    Xu Liang, Ming Huang & Tao Ning

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  1. Xu Liang
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  2. Ming Huang
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  3. Tao Ning
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Correspondence to Tao Ning.

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Liang, X., Huang, M. & Ning, T. Flexible job shop scheduling based on improved hybrid immune algorithm. J Ambient Intell Human Comput 9, 165–171 (2018). https://doi.org/10.1007/s12652-016-0425-9

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  • DOI: https://doi.org/10.1007/s12652-016-0425-9

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