Utility Preference Robust Optimization with Moment-Type Information Structure
针对决策者真实效用函数信息不完全带来的建模风险，提出一种最大最小效用偏好鲁棒优化模型，用矩条件刻画偏好信息，并设计分段线性近似和混合整数线性规划求解方法，数值实验验证了模型和算法的有效性。
Utility Preference Robust Optimization with Moment-Type Information Structure In some decision-making problems, information on the true utility function of the decision maker may be incomplete, which may bring potential modeling risk. In “Utility Preference Robust Optimization with Moment-Type Information Structure,” Guo, Xu, and Zhang propose a maximin utility preference robust optimization model where information about the DM’s preference is constructed by moment-type conditions. The authors propose a piecewise linear approximation approach to tackle the maximin problem, reformulate the approximate problem as a single mixed integer linear program, and derive error bounds for the approximate ambiguity set, the optimal value, and the optimal solutions. To examine the performance of the model and the computational schemes, they carry out extensive numerical tests and demonstrate the effectiveness of the model and efficiency of the computational methods.