Making a Case for Robust Optimization Models
指出传统随机线性规划在存在廉价鲁棒点时仍可能找不到鲁棒解，并讨论分段线性化的局限，主张风险厌恶决策者应在模型中纳入凹效用函数，以电信和财务规划为例。
Robust optimization searches for recommendations that are relatively immune to anticipated uncertainty in the problem parameters. Stochasticities are addressed via a set of discrete scenarios. This paper presents applications in which the traditional stochastic linear program fails to identify a robust solution—despite the presence of a cheap robust point. Limitations of piecewise linearization are discussed. We argue that a concave utility function should be incorporated in a model whenever the decision maker is risk averse. Examples are taken from telecommunications and financial planning.