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首页> 外文期刊>Applied Mathematical Modelling >Robust parameter design in resource-constrained environments: An investigation of trade-offs between costs and precision within variable processes
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Robust parameter design in resource-constrained environments: An investigation of trade-offs between costs and precision within variable processes

机译：在资源受限的环境中进行可靠的参数设计：研究可变过程中成本与精度之间的权衡

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摘要

Engineers and scientists often identify robust parameter design as one of the most important process and quality improvement methods. Focused on statistical modeling and numerical optimization strategies, most researchers typically assume a process with reasonably small variability. Realistically, however, industrial processes often exhibit larger variability, particularly in mass production lines. In such cases, many of the modeling assumptions behind the robust parameter design models available in the literature do not hold. Accordingly, the results and recommendations provided to decision makers could generate suboptimal modifications to processes and products. As manufacturers seek improved methods for ensuring quality in resource-constrained environments, experimenters should examine trade-offs to achieve the levels of precision that best support their decision making. In contrast to previous research, this paper proposes a trade-off analysis between the cost of replication and the desired precision of generated solutions. We consider several techniques in the early stages of experimental design, using Monte Carlo simulation as a tool, for revealing potential options to the decision maker. This is perhaps the first study to show the avenue which may lead to more effective robust parameter design models with the optimal combination of cost constraints and desired precision of solutions.
机译：工程师和科学家经常将健壮的参数设计视为最重要的过程和质量改进方法之一。专注于统计建模和数值优化策略，大多数研究人员通常假设过程具有相当小的变异性。然而，实际上，工业过程通常表现出较大的可变性，尤其是在大规模生产线中。在这种情况下，文献中提供的健壮参数设计模型背后的许多建模假设都不成立。因此，提供给决策者的结果和建议可能会对流程和产品产生次佳的修改。当制造商寻求改进的方法来确保资源受限的环境中的质量时，实验人员应权衡取舍，以达到最能支持其决策的精度水平。与以前的研究相比，本文提出了一种在复制成本与所需生成解决方案精度之间进行权衡的分析方法。我们在实验设计的早期阶段考虑了几种技术，这些技术使用蒙特卡洛模拟作为工具，向决策者揭示潜在的选择。这也许是第一个显示此途径的研究，该途径可能会导致更有效的鲁棒参数设计模型，以及成本约束和所需解决方案精度的最佳组合。

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