Robust Control of Partially Observable Failing Systems
Robust Control of Partially Observable Failing Systems
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DOI：
10.1287/opre.2016.1495
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
M. J. Kim
中科院分区：
文献类型：
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作者：
M. J. Kim
本文研究了模型误设定情况下的最优维修决策问题。具体来说，我们感兴趣的情况下，决策者担心名义贝叶斯模型可能是错误的指定或不切实际的，并希望找到的政策，即使工作的基础模型是有缺陷的。为此，我们制定了一个强大的动态优化模型的条件为基础的维护，决策者明确占名义贝叶斯模型的不信任，通过解决最坏情况下的问题，对第二个代理，“自然”，谁有能力改变底层模型分布在对抗的方式。我们分析的主要重点是建立结构属性和见解，在面对模型的失误规格。特别是，我们证明了（i）一个明确的性质的最优响应，通过分析的鲁棒动态规划方程，（ii）凸性结果的鲁棒值…
This paper is concerned with optimal maintenance decision making in the presence of model misspecification. Specifically, we are interested in the situation where the decision maker fears that a nominal Bayesian model may be miss-specified or unrealistic, and would like to find policies that work well even when the underlying model is flawed. To this end, we formulate a robust dynamic optimization model for condition-based maintenance in which the decision maker explicitly accounts for distrust in the nominal Bayesian model by solving a worst-case problem against a second agent, “nature,” who has the ability to alter the underlying model distributions in an adversarial manner. The primary focus of our analysis is on establishing structural properties and insights that hold in the face of model miss-specification. In particular, we prove (i) an explicit characterization of nature’s optimal response through an analysis of the robust dynamic programming equation, (ii) convexity results for both the robust va...