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<p>In reentrant production, decision makers need to consider whether the part should be discarded or reprocessed. It involves the production and time cost that is required by reprocessing. Therefore, an efficient and feasible assignment method is required for reentrant production. To tackle this issue, we use the Environments-Classes, Agents, Roles, Groups, and Objects model to formalize this problem. A novel solution is designed for Reentrant Production by extending the Group Role Assignment (GRA) problem model to solve the GRA with Balance problem. With this proposed solution, we can get an allocation scheme that takes into account multi-objective optimization and Pareto equilibrium between average performance of the whole reprocessing system and high defect rate parts. Finally, large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution. The simulation results provide a solid decision-making reference for the manufacturer.</p>

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Solving the allocation problem of reentrant production via group role assignment

  • 期刊论文

  • 发表日期：

    2025年11月2日

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  • Kaijia Luo,
  • Haibin Zhu,
  • Dongning Liu

摘要

In reentrant production, decision makers need to consider whether the part should be discarded or reprocessed. It involves the production and time cost that is required by reprocessing. Therefore, an efficient and feasible assignment method is required for reentrant production. To tackle this issue, we use the Environments-Classes, Agents, Roles, Groups, and Objects model to formalize this problem. A novel solution is designed for Reentrant Production by extending the Group Role Assignment (GRA) problem model to solve the GRA with Balance problem. With this proposed solution, we can get an allocation scheme that takes into account multi-objective optimization and Pareto equilibrium between average performance of the whole reprocessing system and high defect rate parts. Finally, large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution. The simulation results provide a solid decision-making reference for the manufacturer.