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首页> 外文期刊>Applied Mathematical Modelling >A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines
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A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines

机译：集成动态蜂窝制造系统和不可靠机器的生产计划的强大优化方法

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摘要

In this study, a robust optimization approach is developed for a new integrated mixed-integer linear programming (MILP) model to solve a dynamic cellular manufacturing system (OCMS) with unreliable machines and a production planning problem simultaneously. This model is incorporated with dynamic cell formation, inter-cell layout, machine reliability, operator assignment, alternative process routings and production planning concepts. To cope with the parts processing time uncertainty, a robust optimization approach immunized against even worst-case is adopted. In fact, this approach enables the system's planner to assess different levels of uncertainty and conservation throughout planning horizon. This study minimizes the costs of machine breakdown and relocation, operator training and hiring, inter-intra cell part trip, and shortage and inventory. To verify the performance of the presented model and proposed approach, some numerical examples are solved in hypothetical limits using the CPLEX solver. The experimental results demonstrate the validity of the presented model and the performance of the developed approach in finding an optimal solution. Finally, the conclusion is presented.
机译：在这项研究中，为新的集成混合整数线性规划（MILP）模型开发了一种鲁棒的优化方法，以同时解决带有不可靠机器的动态蜂窝制造系统（OCMS）和生产计划问题。该模型与动态单元形成，单元间布局，机器可靠性，操作员分配，替代工艺路线和生产计划概念结合在一起。为了应对零件加工时间的不确定性，采用了针对最坏情况的免疫方法。实际上，这种方法使系统的计划者可以在整个计划范围内评估不确定性和保护程度的不同水平。这项研究最大程度地降低了机器故障和重新安置，操作员培训和雇用，内部电池间零件行程以及短缺和库存的成本。为了验证所提出的模型和提出的方法的性能，使用CPLEX求解器在假设的限制下求解了一些数值示例。实验结果证明了所提出模型的有效性以及所开发方法在寻找最佳解决方案方面的性能。最后，给出结论。

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