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A fully parallel multi-objective genetic algorithm for optimization of flexible shop floor production performance and schedule stability under dynamic environments

Author

Listed:
  • Jia Luo

    (Beijing University of Technology
    Beijing University of Technology
    Waseda University)

  • Didier El Baz

    (Université de Toulouse, CNRS)

  • Rui Xue

    (Beijing University of Technology)

  • Jinglu Hu

    (Waseda University)

  • Lei Shi

    (Communication University of China
    Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University), Ministry of Education)

Abstract

As the work environment changes dynamically in real-world manufacturing systems, the dynamic flexible job shop scheduling is an essential problem in operations research. Some works have taken rescheduling approaches to solve it as the multi-objective optimization problem. However, previous studies focus more on solution quality improvements while ignoring computation time. To get a quick response in the dynamic scenario, this paper develops a fully parallel Non-dominated Sorting Genetic Algorithm-II (NSGA-II) on GPUs and uses it to solve the multi-objective dynamic flexible job shop scheduling problem. The mathematical model is NP-hard which considers new arrival jobs and seeks a trade-off between shop efficiency and schedule stability. The proposed algorithm can be executed entirely on GPUs with minimal data exchange while parallel strategies are used to accelerate ranking and crowding mechanisms. Finally, numerical experiments are conducted. As our approach keeps the original structure of the conventional NSGA-II without sacrificing the solutions’ quality, it gains better performance than other GPU-based parallel methods from four metrics. Moreover, a case study of a large-size instance is simulated at the end and displays the conflicting relationship between the two objectives.

Suggested Citation

  • Jia Luo & Didier El Baz & Rui Xue & Jinglu Hu & Lei Shi, 2025. "A fully parallel multi-objective genetic algorithm for optimization of flexible shop floor production performance and schedule stability under dynamic environments," Annals of Operations Research, Springer, vol. 351(1), pages 489-524, August.
    • Handle: RePEc:spr:annopr:v:351:y:2025:i:1:d:10.1007_s10479-025-06482-2
    DOI: 10.1007/s10479-025-06482-2
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    References listed on IDEAS

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    1. Maroua Nouiri & Abdelghani Bekrar & Abderezak Jemai & Smail Niar & Ahmed Chiheb Ammari, 2018. "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 603-615, March.
    2. Gökan May & Bojan Stahl & Marco Taisch & Vittal Prabhu, 2015. "Multi-objective genetic algorithm for energy-efficient job shop scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7071-7089, December.
    3. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    4. Zhongwei Zhang & Lihui Wu & Tao Peng & Shun Jia, 2018. "An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    5. Melissa Shahgholi Zadeh & Yalda Katebi & Ali Doniavi, 2019. "A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3020-3035, May.
    6. Marco A. Boschetti & Vittorio Maniezzo & Francesco Strappaveccia, 2016. "Using GPU Computing for Solving the Two-Dimensional Guillotine Cutting Problem," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 540-552, August.
    7. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.
    8. Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    9. Jobish Vallikavungal Devassia & M. Angélica Salazar-Aguilar & Vincent Boyer, 2018. "Flexible job-shop scheduling problem with resource recovery constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 56(9), pages 3326-3343, May.
    10. G. Ortega & E. Filatovas & E. M. Garzón & L. G. Casado, 2017. "Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU," Journal of Global Optimization, Springer, vol. 69(3), pages 607-627, November.
    11. Adel Kacem & Abdelaziz Dammak, 2021. "Multi-objective scheduling on two dedicated processors," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 694-721, October.
    12. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    13. Audet, Charles & Bigeon, Jean & Cartier, Dominique & Le Digabel, Sébastien & Salomon, Ludovic, 2021. "Performance indicators in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 292(2), pages 397-422.
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