Data-driven remanufacturing planning with parameter uncertainty
Data-driven remanufacturing planning with parameter uncertainty
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DOI：
10.1016/j.ejor.2023.01.031
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Zhicheng Zhu;Yisha Xiang;Ming Zhao;Yue Shi
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文献类型：
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作者：
Zhicheng Zhu;Yisha Xiang;Ming Zhao;Yue Shi
我们考虑的问题，再制造规划的统计估计误差的存在。确定最佳的再制造时机，首先，需要建模的系统的状态转换。然而，对这些概率的估计常常受到数据不足的影响，而且很不准确，导致性能严重下降。为了减轻转移概率的不确定性的影响，我们开发了一种新的数据驱动的再制造规划建模框架，决策者可以保持稳健的统计估计误差。我们建模的再制造规划问题作为一个强大的马尔可夫决策过程，并构建模糊集，包含真正的转移概率具有高的信心。我们进一步建立最优鲁棒策略的结构属性，并为再制造规划提供见解。对NASA涡扇发动机的计算研究表明，与使用转移概率的最大似然估计而不考虑参数不确定性的标称模型相比，我们的数据驱动的鲁棒决策框架始终产生更好的样本外回报和更高的性能保证可靠性。
We consider the problem of remanufacturing planning in the presence of statistical estimation errors. Determining the optimal remanufacturing timing, first and foremost, requires modeling of the state transitions of a system. The estimation of these probabilities, however, often suffers from data inadequacy and is far from accurate, resulting in serious degradation in performance. To mitigate the impacts of the uncertainty in transition probabilities, we develop a novel data-driven modeling framework for remanufacturing planning in which decision makers can remain robust with respect to statistical estimation errors. We model the remanufacturing planning problem as a robust Markov decision process, and construct ambiguity sets that contain the true transition probabilities with high confidence. We further establish structural properties of optimal robust policies and provide insights for remanufacturing planning. A computational study on the NASA turbofan engine shows that our data-driven robust decision framework consistently yields better out-of-sample reward and higher reliability of the performance guarantee, compared to the nominal model that uses the maximum likelihood estimates of the transition probabilities without considering parameter uncertainty.