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A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Author

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  • Fuqing Zhao
  • Zhongshi Shao
  • Junbiao Wang
  • Chuck Zhang

Abstract

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.

Suggested Citation

  • Fuqing Zhao & Zhongshi Shao & Junbiao Wang & Chuck Zhang, 2016. "A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1039-1060, February.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:4:p:1039-1060
    DOI: 10.1080/00207543.2015.1041575
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    Citations

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    Cited by:

    1. Min Dai & Ziwei Zhang & Adriana Giret & Miguel A. Salido, 2019. "An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints," Sustainability, MDPI, vol. 11(11), pages 1-23, May.
    2. Lulu Song & Ying Meng & Qingxin Guo & Xinchang Gong, 2023. "Improved Differential Evolution Algorithm for Slab Allocation and Hot-Rolling Scheduling Integration Problem," Mathematics, MDPI, vol. 11(9), pages 1-19, April.
    3. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.
    4. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    5. Margarita Antoniou & Gregor Papa, 2021. "Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization," Mathematics, MDPI, vol. 9(17), pages 1-22, September.
    6. Gao, Shujun & de Silva, Clarence W., 2018. "Estimation distribution algorithms on constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 323-345.
    7. Mohd Shareduwan Mohd Kasihmuddin & Mohd. Asyraf Mansor & Md Faisal Md Basir & Saratha Sathasivam, 2019. "Discrete Mutation Hopfield Neural Network in Propositional Satisfiability," Mathematics, MDPI, vol. 7(11), pages 1-21, November.

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