Robust optimization: Sensitivity to uncertainty in scalar and vector cases, with applications
Robust optimization: Sensitivity to uncertainty in scalar and vector cases, with applications
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鲁棒优化：对标量和向量情况下的不确定性的敏感性及其应用
DOI：
10.1016/j.orp.2018.03.001
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发表时间：
2018
影响因子：
2.5
通讯作者：
Rocca Matteo
中科院分区：
文献类型：
--
作者：
Crespi Giovanni P.;Kuroiwa Daishi;Rocca Matteo
我们要解决的问题是鲁棒解决方案如何对不确定性集合的变化做出反应。我们证明了鲁棒解相对于不确定性可能减少的幅度的位置，即当不确定性集收缩时，鲁棒解序列的收敛性。在决策中，不确定性可能源于人们（利益相关者，选民，意见领袖等）的不完整信息。对一个具体问题的看法。无论决策者（DM）是否必须寻求董事会的批准或通过一项法案，他们可能需要定义缓解少数人的策略。在这样的问题中，可行域很可能是不变的，而不确定性影响目标函数。因此，本文只研究这一框架。
The question we address is how robust solutions react to changes in the uncertainty set. We prove the location of robust solutions with respect to the magnitude of a possible decrease in uncertainty, namely when the uncertainty set shrinks, and convergence of the sequence of robust solutions.In decision making, uncertainty may arise from incomplete information about people’s (stakeholders, voters, opinion leaders, etc.) perception about a specific issue. Whether the decision maker (DM) has to look for the approval of a board or pass an act, they might need to define the strategy that displeases the minority. In such a problem, the feasible region is likely to unchanged, while uncertainty affects the objective function. Hence the paper studies only this framework.