Production System Efficiency Optimization Using Hybrid AI Solution and Sensor Data

Abstract

Achieving operational excellence and efficiency is a constant goal in manufacturing and production, driven by the need to reduce waste and optimize processes in today’s competitive environment. The era of Industry 4.0 offers advanced technologies such as Artificial Intelligence (AI), robotics, IoT and cloud computing to support this pursuit, aligning with philosophies like lean manufacturing. However, modeling and optimizing complex production systems, which involve nonlinear behavior, interdependencies and time delays, remains a significant challenge. This research proposes a hybrid AI solution for optimizing production efficiency by combining Data Envelopment Analysis (DEA), Machine Learning (ML)-based simulation and Genetic Algorithms (GA). DEA is used to identify the efficient frontier of the production system based on historical or synthetic data. This data is used to train an ML model capable of simulating system behavior. GA is then applied to search for the optimal input configurations and control parameters to maximize efficiency, considering operational constraints. The methodology was applied and validated using real sensor data from a thermoelectric power plant. The process included data preparation with outlier removal using the Z-score method, calculation of relative efficiency using DEA-CCR, training ML models to predict outputs and efficiencies, and applying GA to identify ideal settings. The implementation resulted in significant improvements, with efficiency gains of 25% and 58% in two tested models, outperforming conventional closed-loop control systems. The approach also demonstrated that separating the optimization phase using GA from the DEA and ML training phases can significantly reduce computational time. As a result, the solution not only optimizes resource use and reduces production costs but also provides greater flexibility to production systems by reducing the direct dependency between high output levels and overall efficiency. The developed framework presents a practical, accessible and effective solution for production systems. It is adaptable to various industrial sectors and contributes to the data-driven digital transformation within the scope of Industry 4.0.