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Lean optimization using supersaturated experimental design

机译：使用过饱和实验设计的精益优化

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In practice, product development implies studying numerous factors that affect the final product quality and define its cost. The selection of factors to study has been left to engineers. This work is an attempt for an opposite approach which does not require selecting a small subset of factors explicitly but allows us to briefly investigate most of the parameters using a limited number of experiments. Even more, we assume that the number of experiments can be smaller than the number of parameters. The paper focuses on statistical optimization using supersaturated experimental design. The authors present a new algorithm for exploring a multi-parameter system and performing a lean optimization procedure without spending a lot of efforts. The algorithm aims at making the industrial experimental process more efficient both from the resource consumption and the economic point of view. This is especially important in first stages of system analysis and has therefore practical application in industry where each experiment is very expensive and time-consuming. Numerical results demonstrate efficiency of the algorithm which has been tested for both theoretical and realistic models.
机译：实际上，产品开发意味着研究影响最终产品质量并确定其成本的众多因素。研究因素的选择留给工程师。这项工作是对另一种方法的尝试，该方法不需要明确地选择一小部分因子，但是允许我们使用有限数量的实验来简要地研究大多数参数。甚至更多，我们假设实验的数量可以小于参数的数量。本文着重于使用过饱和实验设计的统计优化。作者提出了一种新算法，用于探索多参数系统并执行精益优化程序，而无需花费很多精力。从资源消耗和经济角度来看，该算法旨在使工业实验过程更加高效。这在系统分析的第一阶段特别重要，因此在每个实验都非常昂贵且耗时的工业中具有实际应用。数值结果证明了该算法的有效性，该算法已针对理论模型和实际模型进行了测试。

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