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Dual-Resource Scheduling with Improved Forensic-Based Investigation Algorithm in Smart Manufacturing

Author

Listed:
  • Yuhang Zeng

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Ping Lou

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Jianmin Hu

    (School of Information Engineering, Hubei University of Economics, Wuhan 430205, China
    Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan 430205, China)

  • Chuannian Fan

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Quan Liu

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Jiwei Hu

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. In addition, a two-stage approach based on a forensic-based investigation (TSFBI) is proposed to solve the problem. In the first stage, an improved multi-objective FBI algorithm is used to obtain the Pareto front solutions of this model, in which a hybrid real and integer encoding–decoding method is used for exploring the solution space and a fast non-dominated sorting method for improving efficiency. In the second stage, a multi-criteria decision analysis method based on an analytic hierarchy process (AHP) is used to select the optimal solution from the Pareto front solutions. Finally, experiments validated the TSFBI algorithm, showing its potential for smart manufacturing.

Suggested Citation

  • Yuhang Zeng & Ping Lou & Jianmin Hu & Chuannian Fan & Quan Liu & Jiwei Hu, 2025. "Dual-Resource Scheduling with Improved Forensic-Based Investigation Algorithm in Smart Manufacturing," Mathematics, MDPI, vol. 13(9), pages 1-30, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1432-:d:1643874
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    References listed on IDEAS

    as
    1. Julio Mar-Ortiz & Alex J. Ruiz Torres & Belarmino Adenso-Díaz, 2022. "Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier," Operational Research, Springer, vol. 22(4), pages 4585-4605, September.
    2. Seifi, Cinna & Schulze, Marco & Zimmermann, Jürgen, 2021. "A new mathematical formulation for a potash-mine shift scheduling problem with a simultaneous assignment of machines and workers," European Journal of Operational Research, Elsevier, vol. 292(1), pages 27-42.
    3. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    4. Nhat-Duc Hoang & Thanh-Canh Huynh & Van-Duc Tran & Gonzalo Farias, 2021. "Computer Vision-Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic-Based Investigation Metaheuristic," Complexity, Hindawi, vol. 2021, pages 1-17, September.
    5. Johanna Mlekusch & Richard F. Hartl, 2025. "The dual-resource-constrained re-entrant flexible flow shop a constraint programming approach and a hybrid genetic algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 63(5), pages 1803-1824, March.
    6. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    7. Haibo Wang & Bahram Alidaee & Jaime Ortiz & Wei Wang, 2021. "The multi-skilled multi-period workforce assignment problem," International Journal of Production Research, Taylor & Francis Journals, vol. 59(18), pages 5477-5494, September.
    8. Dashuang Li & Chaoyong Zhang & Xinyu Shao & Wenwen Lin, 2016. "A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 725-739, August.
    9. Dimitris Mourtzis, 2020. "Simulation in the design and operation of manufacturing systems: state of the art and new trends," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 1927-1949, April.
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