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A deep Q network algorithm for a car resequencing problem in automobile factories

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Listed:
  • Weiya Zhong

    (Shanghai University)

  • Yechen Yang

    (Shanghai University)

  • Dedi Ye

    (Shanghai University)

  • Ningna Bi

    (Shanghai University)

Abstract

In this paper a car resequencing problem between the paint shop and the assembly shop in an automobile manufacturing factory is studied. Key characteristics of each car body include color, power type and drive type. Since each shop has different production preferences and constraints, they cannot work according to the same sequence, which requires the establishment of a painted body store (PBS). PBS is used to adjust the outgoing sequence of the paint shop to the incoming sequence that meets the constraints of the assembly shop (maximizing the reward value according to certain rules). An MDP model incorporating the objective function into the definition of the states is constructed and a deep Q network algorithm (DQN $$_0$$ 0 ) is developed to solve this problem. Greedy algorithms and another deep Q network algorithm based on an alternative MDP model (DQN $$_1$$ 1 ) are also designed. Numerical experiments are carried out and the results show that (1) DQN $$_0$$ 0 algorithm can obtain a solution very fast; (2) it always outperforms the greedy algorithms; (3) DQN $$_0$$ 0 can obtain a solution as good as DQN $$_1$$ 1 , but it runs much faster.

Suggested Citation

  • Weiya Zhong & Yechen Yang & Dedi Ye & Ningna Bi, 2025. "A deep Q network algorithm for a car resequencing problem in automobile factories," Flexible Services and Manufacturing Journal, Springer, vol. 37(3), pages 730-749, September.
    • Handle: RePEc:spr:flsman:v:37:y:2025:i:3:d:10.1007_s10696-024-09560-y
    DOI: 10.1007/s10696-024-09560-y
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    References listed on IDEAS

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    1. Boysen, Nils & Scholl, Armin & Wopperer, Nico, 2012. "Resequencing of mixed-model assembly lines: Survey and research agenda," European Journal of Operational Research, Elsevier, vol. 216(3), pages 594-604.
    2. Prandtstetter, Matthias & Raidl, Günther R., 2008. "An integer linear programming approach and a hybrid variable neighborhood search for the car sequencing problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 1004-1022, December.
    3. Solnon, Christine & Cung, Van Dat & Nguyen, Alain & Artigues, Christian, 2008. "The car sequencing problem: Overview of state-of-the-art methods and industrial case-study of the ROADEF'2005 challenge problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 912-927, December.
    4. Jinling Leng & Xingyuan Wang & Shiping Wu & Chun Jin & Meng Tang & Rui Liu & Alexander Vogl & Huiyu Liu, 2023. "A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 61(15), pages 5156-5175, August.
    5. Gagne, Caroline & Gravel, Marc & Price, Wilson L., 2006. "Solving real car sequencing problems with ant colony optimization," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1427-1448, November.
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