Production system efficiency optimization through application of a hybrid artificial intelligence solution

Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system's efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution.