含不确定参数线性优化的新鲁棒优化模型

针对椭球不确定数据鲁棒线性优化模型的保守性，提出了一种新的鲁棒线性优化模型。通过引入新的距离公式，把椭球不确定数据映射到单位球中，以此来改进鲁棒线性优化模型。新模型克服了原模型对数据扰动较大时的保守性，从而在解的鲁棒性和最优性之间得到一个比较好的平衡。通过对几个标准实际问题的测试，结果表明新模型在保证解的鲁棒性的同时具有良好的最优性。

New robust optimization counterpart for linear optimization with uncertain data

For the conservative defect of the robust linear optimization model with ellipsoid uncertainty data, a new robust linear optimization formula is proposed. Through introduction of the new distance formula, the ellipsoid uncertainty data are mapped to the unit ball so as to improve the robust linear optimization model. The new model overcomes the conservative defect of the original model when the data perturbations are larger, so the solution of the new model gets a relatively good balance between robustness and optimality. Several standard practical problems are tested; the simulation results show that the new model not only can ensure the robustness of solution but also has good optimality.