Abstract Key performance indicators (KPIs) modeling and control is important for efficient design and operation of complex manufacturing production systems. This paper proposes to implement the KPI control based on KPI modeling and stochastic optimization. The KPI relationship is first approximated using ordered block model and pair-copula construction (OBM-PCC) model, which is a non-parametric model that facilitates a flexible surrogate of the KPI relationship. Then, the KPI control is framed into a stochastic optimization problem, where the randomness in the cost function depends on the decision variables. To solve this stochastic optimization problem, the standard uniform distribution is employed to link the OBM-PCC model and the cost function to transform the problem into an ordinary stochastic optimization problem. The proposed method is efficient in KPI control and the performance is robust to the cost function. Extensive numerical studies and comparisons, together with a case study, are presented to demonstrate the effectiveness of the proposed KPI control framework.

中文翻译：

---

通过pair-copula建模和随机优化控制制造生产系统的关键性能指标

摘要 关键性能指标 (KPI) 建模和控制对于复杂制造生产系统的高效设计和运行非常重要。本文提出基于KPI建模和随机优化来实现KPI控制。KPI 关系首先使用有序块模型和对联结构 (OBM-PCC) 模型进行近似，这是一种非参数模型，有助于 KPI 关系的灵活替代。然后，将 KPI 控制框架化为一个随机优化问题，其中成本函数的随机性取决于决策变量。为了解决这个随机优化问题，采用标准均匀分布将OBM-PCC模型和成本函数联系起来，将问题转化为一个普通的随机优化问题。所提出的方法在 KPI 控制方面是有效的，并且性能对成本函数具有鲁棒性。大量的数值研究和比较，连同一个案例研究，被提出来证明所提出的 KPI 控制框架的有效性。