Towards Sustainable Industry 5.0: An LLM-Based Co-Pilot for Energy-Efficient Factory Scheduling

Abstract

Industry 5.0 promotes sustainable, resilient, and human-centric manufacturing. Many factories struggle to produce energy-aware schedules that balance throughput, energy, and time-of-use (TOU) tariffs. Classical methods (heuristics and optimization) help but lack transparency and adaptability, limiting operator-in-the-loop use. Generative AI, particularly Large Language Models (LLMs), offers reasoning, adaptation, and interaction, yet integration with production scheduling is nascent. We introduce a hybrid framework that combines classical heuristics with GPT-4 reasoning to create an Industry 5.0-compatible Co-Pilot for energy-aware factory scheduling. The Co-Pilot evaluates and adapts machine operation schedules to avoid peak windows and explains trade-offs in natural language. We evaluate on three datasets (CTU synthetic, Kaggle manufacturing, Zenodo benchmark). Results show the heuristic Co-Pilot consistently reduces peak load share versus classical baselines at similar cost; on Zenodo, GPT-4 saves 4–7% in cost and energy, while its performance is less stable on synthetic data. These findings highlight the promise of LLM-powered scheduling and the value of hybrid human-AI strategies in Industry 5.0.