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A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem

Author

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  • Cheng-Dar Liou
  • Yi-Chih Hsieh
  • Yin-Yann Chen

Abstract

This article investigates the two-machine flow-shop group scheduling problem (GSP) with sequence-dependent setup and removal times, and job transportation times between machines. The objective is to minimise the total completion time. As known, this problem is an NP-hard problem and generalises the typical two-machine GSPs. In this article, a new encoding scheme based on permutation representation is proposed to transform a random job permutation to a feasible permutation for GSPs. The proposed encoding scheme simultaneously determines both the sequence of jobs in each group and the sequence of groups. By reasonably combining particle swarm optimisation (PSO) and genetic algorithm (GA), we develop a fast and easily implemented hybrid algorithm (HA) for solving the considered problems. The effectiveness and efficiency of the proposed HA are demonstrated and compared with those of standard PSO and GA by numerical results of various tested instances with group numbers up to 20. In addition, three different lower bounds are developed to evaluate the solution quality of the HA. Limited numerical results indicate that the proposed HA is a viable and effective approach for the studied two-machine flow-shop group scheduling problem.

Suggested Citation

  • Cheng-Dar Liou & Yi-Chih Hsieh & Yin-Yann Chen, 2013. "A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(1), pages 77-93.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:1:p:77-93
    DOI: 10.1080/00207721.2011.581396
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    Citations

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    Cited by:

    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. Shuaipeng Yuan & Tieke Li & Bailin Wang, 2021. "A discrete differential evolution algorithm for flow shop group scheduling problem with sequence-dependent setup and transportation times," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 427-439, February.
    3. Nantiwat Pholdee & Sujin Bureerat, 2016. "Hybrid real-code ant colony optimisation for constrained mechanical design," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(2), pages 474-491, January.
    4. Kuo-Hsiung Wang & Cheng-Dar Liou & Ya-Lin Wang, 2014. "Profit optimisation of the multiple-vacation machine repair problem using particle swarm optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(8), pages 1769-1780, August.
    5. Liou, Cheng-Dar & Hsieh, Yi-Chih, 2015. "A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 258-267.
    6. Maryam Mousavi & Hwa Jen Yap & Siti Nurmaya Musa & Farzad Tahriri & Siti Zawiah Md Dawal, 2017. "Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.

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