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A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown

Author

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  • Kai Wang

    (Economics and Management School,Wuhan University, Wuhan, China)

  • Yun Huang

    (School of Business, Macau University of Science and Technology, Macau, China)

  • Hu Qin

    (School of Management, Huazhong University of Science and Technology, Wuhan, China)

Abstract

As the research interest in distributed scheduling is growing, distributed permutation flowshop scheduling problems (DPFSPs) have recently attracted an increasing attention. This paper presents a fuzzy logic-based hybrid estimation of distribution algorithm (FL-HEDA) to address DPFSPs under machine breakdown with makespan criterion. In order to explore more promising search space, FL-HEDA hybridises the probabilistic model of estimation of distribution algorithm with crossover and mutation operators of genetic algorithm to produce new offspring. In the FL-HEDA, a novel fuzzy logic-based adaptive evolution strategy (FL-AES) is adopted to preserve the population diversity by dynamically adjusting the ratio of offspring generated by the probabilistic model. Moreover, a discrete-event simulator that models the production process under machine breakdown is applied to evaluate expected makespan of offspring individuals. The simulation results show the effectiveness of FL-HEDA in solving DPFSPs under machine breakdown.

Suggested Citation

  • Kai Wang & Yun Huang & Hu Qin, 2016. "A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(1), pages 68-82, January.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:1:p:68-82
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    Cited by:

    1. Pourya Pourhejazy & Chen-Yang Cheng & Kuo-Ching Ying & Nguyen Hoai Nam, 2023. "Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups," Annals of Operations Research, Springer, vol. 322(1), pages 125-146, March.
    2. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    3. Ruiz, Rubén & Pan, Quan-Ke & Naderi, Bahman, 2019. "Iterated Greedy methods for the distributed permutation flowshop scheduling problem," Omega, Elsevier, vol. 83(C), pages 213-222.

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