The last decade witnessed an explosion in the availability of data for
operations research applications. Motivated by this growing availability, we
propose a novel schema for utilizing data to design uncertainty sets for robust
optimization using statistical hypothesis tests. The approach is flexible and
widely applicable, and robust optimization problems built from our new sets are
computationally tractable, both theoretically and practically. Furthermore,
optimal solutions to these problems enjoy a strong, finite-sample probabilistic
guarantee. \edit{We describe concrete procedures for choosing an appropriate
set for a given application and applying our approach to multiple uncertain
constraints. Computational evidence in portfolio management and queuing confirm
that our data-driven sets significantly outperform traditional robust
optimization techniques whenever data is available.

提出了一种新的统计假设检验方法来利用数据设计鲁棒优化的不确定集合，该方法灵活适用于各种领域，并且计算复杂度可行，理论和实践上都具有可靠性。操作研究领域的计算实验证明，当数据可用时，与传统的鲁棒优化技术相比，我们的数据驱动集合显著优于传统技术。