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 designs. 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：本文讨论并举例说明了从文献中随机实现某些极小极大稳健设计的方法。相较于确定性设计，这些方法具有优势，即在实验者所拟合的几乎必然不精确的响应模型的大且丰富的邻域内，其最大损失有界。它们的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，这些方法被推广到更一般的稳健设计。对于二维设计，我们从基于所选基点生成的Voronoi剖分收缩中进行采样，该剖分划分了设计空间。这些思想进一步推广到一般$k$维设计。