Robust optimization approaches are commonly applied in solving a problem with uncertainty. One of the main issues in dealing with uncertainties is the correlations among uncertain parameters. This subject is rarely considered by the researchers and hence the proposed robust approaches normally lead to the solutions which include perturbations with low probability of occurrence. This results in solutions with over conservatism. In this research, an estimation of correlation matrix is applied in order to provide a new uncertainty set that includes probable perturbations. Furthermore in order to trade-off between optimality and level of robustness, a decision making parameter is applied to formulate the corresponding robust counterpart. In order to study the performance of the proposed method, an uncertain optimization problem with correlated uncertain coefficients is solved. Results of the study reveal that the proposed model has superior performance than that of the existing robust approaches.

鲁棒的优化方法通常用于解决不确定性问题。处理不确定性的主要问题之一是不确定参数之间的相关性。研究人员很少考虑这个问题，因此，所提出的鲁棒方法通常会导致解决方案，其中包括发生概率较低的扰动。这导致解决方案过于保守。在这项研究中，为了提供一个新的不确定性集，其中包括可能的扰动，应用了相关矩阵的估计。此外，为了在最优性和鲁棒性之间权衡，应用决策参数来制定相应的鲁棒对应物。为了研究所提出方法的性能，解决了具有相关不确定系数的不确定优化问题。研究结果表明，所提出的模型比现有的鲁棒方法具有更好的性能。