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Multi-stage distributionally robust optimization with risk aversion
Journal of Industrial and Management Optimization ( IF 1.3 ) Pub Date : 2019-09-27 , DOI: 10.3934/jimo.2019109
Ripeng Huang , , Shaojian Qu , Xiaoguang Yang , Zhimin Liu , ,

Two-stage risk-neutral stochastic optimization problem has been widely studied recently. The goals of our research are to construct a two-stage distributionally robust optimization model with risk aversion and to extend it to multi-stage case. We use a coherent risk measure, Conditional Value-at-Risk, to describe risk. Due to the computational complexity of the nonlinear objective function of the proposed model, two decomposition methods based on cutting planes algorithm are proposed to solve the two-stage and multi-stage distributional robust optimization problems, respectively. To verify the validity of the two models, we give two applications on multi-product assembly problem and portfolio selection problem, respectively. Compared with the risk-neutral stochastic optimization models, the proposed models are more robust.

中文翻译:

具有风险规避的多阶段分布鲁棒优化

两阶段风险中性随机优化问题最近得到了广泛的研究。我们研究的目标是构建具有风险规避的两阶段分布鲁棒优化模型,并将其扩展到多阶段案例。我们使用一致的风险度量(条件风险值)来描述风险。由于该模型非线性目标函数的计算复杂性,提出了两种基于割平面算法的分解方法分别解决两阶段和多阶段分布鲁棒优化问题。为了验证两个模型的有效性,我们分别针对多产品装配问题和投资组合选择问题给出了两个应用。与风险中性随机优化模型相比,所提出的模型更加健壮。
更新日期:2019-09-27
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