Application of kernel ABC to build a digital twin of the manufacturing factory

Abstract

In the uncertain and unstable era of recent years, it is crucial to building a digital twin using queue simulation for factories, logistics, and supply chains in the manufacturing and logistics industry and perform optimal operations. However, it has been difficult to optimize the parameters of discrete queuing simulation to reproduce the data as an actual situation. Therefore, we solved this problem by using a data assimilation method that extends kernel ABC. While the parameters of the simulation are usually determined as deterministic parameters, this method can determine stochastic parameters. This method can be interpretable as determining the parameters of the deductive simulation model by an inductive machine learning method. We will introduce the mathematical background of this method and examples of applying it to actual manufacturing and logistics companies.