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Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization

机译:健壮的数学模型优化设计,以考虑劳动力利用的动态细胞形成问题

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Cell formation (CF) problem is one of the most important decision problems in designing a cellular manufacturing system includes grouping machines into machine cells and parts into part families. Several factors should be considered in a cell formation problem. In this work, robust optimization of a mathematical model of a dynamic cell formation problem integrating CF, production planning and worker assignment is implemented with uncertain scenario-based data. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all possible future scenarios. In this research, miscellaneous cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer nonlinear programming model is developed to formulate the related robust dynamic cell formation problem. The objective function seeks to minimize total costs including machine constant, machine procurement, machine relocation, machine operation, inter-cell and intra-cell movement, overtime, shifting labors between cells and inventory holding. Finally, a case study is carried out to display the robustness and effectiveness of the proposed model. The tradeoff between solution robustness and model robustness is also analyzed in the obtained results.
机译:单元形成(CF)问题是设计蜂窝制造系统中最重要的决策问题之一,其中包括将机器分组为机器单元,将零件分组为零件族。在细胞形成问题中应考虑几个因素。在这项工作中,使用不确定的基于场景的数据对集成了CF,生产计划和工人分配的动态单元形成问题的数学模型进行了稳健的优化。对于所有可能的未来方案,使用鲁棒的方法来减少不确定参数波动的影响。在这项研究中,细胞形成和需求波动的杂项成本参数都受到不确定性的影响,并建立了一个混合整数非线性规划模型来表达相关的鲁棒动态细胞形成问题。目标功能旨在使总成本最小化,包括机器常数,机器采购,机器重新定位,机器操作,小区间和小区间移动,加班,在小区间间转移劳动力和库存保持。最后,进行了案例研究以显示所提出模型的鲁棒性和有效性。在所得结果中还分析了解决方案鲁棒性与模型鲁棒性之间的折衷。

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