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Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm

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  • Yuyan Han
  • Dunwei Gong
  • Junqing Li
  • Yong Zhang

Abstract

The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies.

Suggested Citation

  • Yuyan Han & Dunwei Gong & Junqing Li & Yong Zhang, 2016. "Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 54(22), pages 6782-6797, November.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:22:p:6782-6797
    DOI: 10.1080/00207543.2016.1177671
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    Cited by:

    1. Li, Guo & Li, Na & Sambandam, Narayanasamy & Sethi, Suresh P. & Zhang, Faping, 2018. "Flow shop scheduling with jobs arriving at different times," International Journal of Production Economics, Elsevier, vol. 206(C), pages 250-260.
    2. Marcelo Seido Nagano & Adriano Seiko Komesu & Hugo Hissashi Miyata, 2019. "An evolutionary clustering search for the total tardiness blocking flow shop problem," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1843-1857, April.
    3. Xiaohui Zhang & Xinhua Liu & Shufeng Tang & Grzegorz Królczyk & Zhixiong Li, 2019. "Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 12(17), pages 1-24, August.
    4. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.

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