论文标题
内源性不确定性下的多阶段可靠的混合企业优化
Multistage Robust Mixed-Integer Optimization Under Endogenous Uncertainty
论文作者
论文摘要
内生，即决策依赖性的不确定性对随机编程社区产生了越来越多的兴趣。但是，在强大的优化环境中，很少考虑它。这项工作解决了多阶段鲁棒的混合企业优化，并与决策相关的不确定性集。提出的框架使我们能够考虑连续和整数追索权，包括影响不确定性集的追索决策。我们通过利用构建非线性决策规则的最新进展，并引入不连续的分段线性决策规则来得出问题的重新重新制定。进行计算实验是为了了解内源性不确定性的影响，离散追索权的好处和计算绩效的见解。我们的结果表明，如果对内源性不确定性和混合企业追索权进行正确建模，则解决方案中的保守性水平可以显着降低。
Endogenous, i.e. decision-dependent, uncertainty has received increased interest in the stochastic programming community. In the robust optimization context, however, it has rarely been considered. This work addresses multistage robust mixed-integer optimization with decision-dependent uncertainty sets. The proposed framework allows us to consider both continuous and integer recourse, including recourse decisions that affect the uncertainty set. We derive a tractable reformulation of the problem by leveraging recent advances in the construction of nonlinear decision rules, and introduce discontinuous piecewise linear decision rules for continuous recourse. Computational experiments are performed to gain insights on the impact of endogenous uncertainty, the benefit of discrete recourse, and computational performance. Our results indicate that the level of conservatism in the solution can be significantly reduced if endogenous uncertainty and mixed-integer recourse are properly modeled.
