论文标题
内源性不确定性的统一框架，可调节可调式优化
A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty
论文作者
论文摘要
这项工作提出了一个可调节鲁棒优化的框架，该框架统一了三种不同类型的内源性不确定性处理的处理，在这种情况下，决策分别（i）改变了不确定性集，（ii）影响不确定参数的物质化，（iii）确定不确定参数的真实值的时间。我们对内源性不确定性的不同类型的内源性不确定性提供了系统分析，并突出了内源性不确定性和主动学习下的优化之间的联系。我们考虑依赖决策的多面体不确定性集，并提出一种结合连续和二进制追索权的决策规则方法，包括影响不确定性集的求助决策。所提出的方法可以实现决策依赖性的非那能建模，并导致对问题的可行重新制定。我们证明了该方法在涵盖一系列应用的计算实验中的有效性，包括植物重新设计，通过检查进行维护计划，优化容量计划中的修订点以及具有主动参数估计的生产计划。结果表明，内源性不确定性和主动学习的适当建模可取得重大好处。
This work proposes a framework for multistage adjustable robust optimization that unifies the treatment of three different types of endogenous uncertainty, where decisions, respectively, (i) alter the uncertainty set, (ii) affect the materialization of uncertain parameters, and (iii) determine the time when the true values of uncertain parameters are observed. We provide a systematic analysis of the different types of endogenous uncertainty and highlight the connection between optimization under endogenous uncertainty and active learning. We consider decision-dependent polyhedral uncertainty sets and propose a decision rule approach that incorporates both continuous and binary recourse, including recourse decisions that affect the uncertainty set. The proposed method enables the modeling of decision-dependent nonanticipativity and results in a tractable reformulation of the problem. We demonstrate the effectiveness of the approach in computational experiments that cover a range of applications, including plant redesign, maintenance planning with inspections, optimizing revision points in capacity planning, and production scheduling with active parameter estimation. The results show significant benefits from the proper modeling of endogenous uncertainty and active learning.
