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A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem

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  • Antonio Costa

    (University of Catania)

  • Fulvio Antonio Cappadonna

    (University of Catania)

  • Sergio Fichera

    (University of Catania)

Abstract

In this paper, the flow-shop sequence-dependent group scheduling (FSDGS) problem is addressed with reference to the makespan minimization objective. In order to effectively cope with the issue at hand, a hybrid metaheuristic procedure integrating features from genetic algorithms and random sampling search methods has been developed. The proposed technique makes use of a matrix encoding able to simultaneously manage the sequence of jobs within each group and the sequence of groups to be processed along the flow-shop manufacturing system. A well-known problem benchmark arisen from literature, made by two, three and six-machine instances has been taken as reference for both tuning the relevant parameters of the proposed procedure and assessing performances of such approach against the two most recent algorithms presented in the body of literature addressing the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGS problem under investigation.

Suggested Citation

  • Antonio Costa & Fulvio Antonio Cappadonna & Sergio Fichera, 2017. "A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1269-1283, August.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:6:d:10.1007_s10845-015-1049-1
    DOI: 10.1007/s10845-015-1049-1
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Dung-Ying Lin & Tzu-Yun Huang, 2021. "A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    3. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    4. He, Xuan & Pan, Quan-Ke & Gao, Liang & Neufeld, Janis S., 2023. "An asymmetric traveling salesman problem based matheuristic algorithm for flowshop group scheduling problem," European Journal of Operational Research, Elsevier, vol. 310(2), pages 597-610.
    5. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    6. Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
    7. Shruti Shashikumar & Rakesh D. Raut & Vaibhav S. Narwane & Bhaskar B. Gardas & Balkrishna E. Narkhede & Anjali Awasthi, 2019. "A novel approach to determine the cell formation using heuristics approach," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 628-656, September.
    8. Hongfeng Wang & Min Huang & Junwei Wang, 2019. "An effective metaheuristic algorithm for flowshop scheduling with deteriorating jobs," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2733-2742, October.

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