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Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution

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Abstract

In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan and the largest job delay are treated as two separate objectives which are optimized simultaneously. To improve the performance of the proposed algorithm and eliminate the difficulty of setting parameters, an adaptive mechanism is designed and incorporated into DE. Moreover, various local search and hybrid meta-heuristic methods are presented and compared to improve the convergence. Through the analysis of the experimental results, the proposed algorithm is able to tackle the FSSPwLB problems effectively by generating superior and stable scheduling strategies.

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Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, China

    Jing Liang, Peng Wang, Li Guo, Caitong Yue & Kunjie Yu

  2. School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

  3. School of Civil Engineering, Henan Polytechnic University, Jiaozuo, China

    Yachao Wang

Authors
  1. Jing Liang
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  2. Peng Wang
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  3. Li Guo
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  4. Boyang Qu
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  5. Caitong Yue
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  6. Kunjie Yu
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  7. Yachao Wang
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Corresponding author

Correspondence to Boyang Qu.

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Funding

The work is supported by National Natural Science Foundation of China (61876169, 61673404, 61473266, and 61806179), Project supported by the Research Award Fund for Outstanding Young Teachers in Henan Provincial Institutions of Higher Education of China (2014GGJS-004 and 2016GGJS-094) and Program for Science & Technology Innovation Talents in Universities of Henan Province in China (16HASTIT041 and 16HASTIT033), Scientific and Technological Project of Henan Province (152102210153), China Postdoctoral Science Foundation(2017M622373).

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Liang, J., Wang, P., Guo, L. et al. Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution. Memetic Comp. 11, 407–422 (2019). https://doi.org/10.1007/s12293-019-00290-5

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  • DOI: https://doi.org/10.1007/s12293-019-00290-5

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