Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

Abstract

The paper presents possible approaches for reducing the volume of data generated by simulation optimisation performed with a digital twin created in accordance with the Industry 4.0 concept. The methodology is validated using an application developed for controlling the execution of parallel simulation experiments (using client–server architecture) with the digital twin. The paper describes various pseudo-gradient, stochastic, and metaheuristic methods used for finding the global optimum without performing a complete pruning of the search space. The remote simulation optimisers reduce the volume of generated data by hashing the data. The data are sent to a remote database of simulation experiments for the digital twin for use by other simulation optimisers.