文章以随机规划中的机会约束思想为指导，根据随机参数的概率分布情况，提出了两种鲁棒性条件约束，并在此基础上建立了一种新的鲁棒优化模型，使模型的可行解控制在一定的鲁棒性指标的范围内。该模型不但可处理约束两端同时含有随机参数的情况，还可以方便地推广到非线性模型中。仿真实例说明了模型的有效性。

Robust optimization is a method to process optimization problem under uncertainty. The current robust optimization methods have some deficiencies in application conditions and probability utilization. Based on the chance constraints programming, two kinds of robust constraints according to two different kinds of probability distribution of the stochastic parameters are proposed, and a novel robust optimization model is proposed. The feasible solutions of this model can be controlled to satisfy the robust index. This model can be used in the situations that both sides of the constraints contain stochastic parameters, and can be easily extended to non-liner models. The simulation results illustrate the validity of the model.