We discuss, and give examples of, methods for randomly implementing some minimax robust designs from the literature. These have the advantage, over their deterministic counterparts, of having bounded maximum loss in large and very rich neighbourhoods of the, almost certainly inexact, response model fitted by the experimenter. Their maximum loss rivals that of the theoretically best possible, but not implementable, minimax design. The procedures are then extended to more general robust designs. For two-dimensional designs we sample from contractions of Voronoi tessellations, generated by selected basis points, which partition the design space. These ideas are then extended to k-dimensional designs for general k.

翻译：我们讨论并举例说明了从文献中随机实现一些极小极大稳健设计的方法。与确定性对应方法相比，这些方法具有一个优势：在实验者拟合的几乎肯定不精确的响应模型的大而丰富的邻域内，它们能保证有界最大损失。其最大损失可与理论上最优但无法实现的极小极大设计相媲美。随后，我们将这些程序扩展到更一般的稳健设计。对于二维设计，我们从由选定基点生成的Voronoi镶嵌的收缩中进行采样，这些镶嵌划分了设计空间。接着，将这些思想扩展到一般k维设计。