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Bi-objective hybrid flow shop scheduling with common due date

Author

Listed:
  • Zhi Li

    (School of Electromechanical Engineering, Guangdong University of Technology
    The University of Hong Kong)

  • Ray Y. Zhong

    (The University of Hong Kong)

  • Ali Vatankhah Barenji

    (School of Electromechanical Engineering, Guangdong University of Technology)

  • J. J. Liu

    (School of Electromechanical Engineering, Guangdong University of Technology)

  • C. X. Yu

    (China University of Petroleum-Beijing)

  • George Q. Huang

    (The University of Hong Kong)

Abstract

In this paper, the problem of hybrid flow shop scheduling with common due dates (HFSCDD) is studied, and the objectives are to minimize the total waiting time and the total earliness/tardiness issues that arise. This study was motivated by a real-life shop floor, with the predefined goal of meeting the requirements of the final product in many manufacturing industries. Where the final product is assembled from multiple components and, the assembly is only initiated when all components of the product are complete in number. These interrelated components have common due dates. In this study, we developed a mathematical model of HFSCDD which made up of “n” jobs that were processed in “m” machines, located on “I” stages by taking into consideration the common due dates. This problem is classified as being NP-hard, and so an efficient modified genetic algorithm is developed to solve it. The proposed modify GA is developed based on the NSGA II method for large sized problems. The results of the proposed algorithm have been compared with PSO and GA algorithms and showed that the proposed algorithm achieved better performance than existing solutions, since the waiting time and the earliness/tardiness are significantly reduced. This is facilitated by the simultaneous production of components for the same product.

Suggested Citation

  • Zhi Li & Ray Y. Zhong & Ali Vatankhah Barenji & J. J. Liu & C. X. Yu & George Q. Huang, 2021. "Bi-objective hybrid flow shop scheduling with common due date," Operational Research, Springer, vol. 21(2), pages 1153-1178, June.
    • Handle: RePEc:spr:operea:v:21:y:2021:i:2:d:10.1007_s12351-019-00470-8
    DOI: 10.1007/s12351-019-00470-8
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    References listed on IDEAS

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    1. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    2. Pan, Quan-Ke & Wang, Ling & Li, Jun-Qing & Duan, Jun-Hua, 2014. "A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation," Omega, Elsevier, vol. 45(C), pages 42-56.
    3. Figielska, Ewa, 2014. "A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages," European Journal of Operational Research, Elsevier, vol. 236(2), pages 433-444.
    4. Grabowski, Jozef & Pempera, Jaroslaw, 2000. "Sequencing of jobs in some production system," European Journal of Operational Research, Elsevier, vol. 125(3), pages 535-550, September.
    5. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    6. Hidri, Lotfi, 2016. "Note on the Hybrid Flowshop Scheduling Problem with Multiprocessor Tasks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 531-534.
    7. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
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