Reinforcement learning for energy-efficient control of parallel and identical machines

Abstract

Nowadays, the growing interest in industry for enhancing the sustainability of manufacturing processes is becoming a major trend. Energy consumption can be lowered by controlling machine states with energy-efficient control policies that switch off/on the device. Recent studies have shown that Reinforcement Learning algorithms can effectively control manufacturing systems without the requirement of prior knowledge about system parameters. This is a significant factor since full information on system dynamics is difficult to obtain in real-world applications. This work proposes a new Reinforcement Learning-based algorithm to apply energy-efficient control strategies to a single workstation consisting of identical parallel machines. The model goal is to achieve the optimum trade-off between system productivity and energy demand without relying on full knowledge of the system dynamics. Numerical experiments confirm effectiveness, applicability, and generality of the proposed approach, even when applied to a real-world industrial system from the automotive sector.