Authors: Anders Bjornstad, Mira Kaur, Hiroshi Nakamura
Journal: International Journal of Smart Manufacturing and Industrial Engineering (IJSMIE), ISSN 3155-9735
Citation: IJSMIE 1(1), 2024-01-31.
Type: Original Research
The increasing demand for energy efficiency in smart manufacturing necessitates advanced scheduling approaches that minimize energy consumption while maintaining productivity. This paper proposes a reinforcement learning (RL)-based framework for energy-efficient scheduling in dynamic manufacturing environments. The framework integrates deep Q-networks (DQN) and proximal policy optimization (PPO) to learn optimal scheduling policies that balance energy use and makespan. We evaluate the framework on a simulated flexible job shop with varying machine speeds and energy profiles. Results demonstrate that the RL-based scheduler reduces energy consumption by up to 18% compared to heuristic baselines (earliest due date, shortest processing time) while preserving throughput. The PPO agent achieves superior stability and convergence. Sensitivity analysis reveals that the energy savings are robust to changes in job arrival rates and machine heterogeneity. This work provides a scalable solution for real-time energy-aware scheduling in Industry 4.0 settings.
reinforcement learning, energy-efficient scheduling, smart manufacturing, deep Q-network, proximal policy optimization, flexible job shop, Industry 4.0