Control of key performance indicators of manufacturing production systems through pair-copula modeling and stochastic optimization

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.