Energy-Efficient Control Policy for Parallel and Identical Machines With Availability Constraint

Abstract

Energy efficiency is becoming a key subject in manufacturing, especially if related to the major environmental impact of machining activities. Nowadays, great research efforts are performed to find new methodologies to improve sustainability of manufacturing processes. Energy consumption can be lowered controlling machine state during idle periods. This can be achieved with Energy-Efficient Control (EEC) policies that switch off/on the machine. The same approach can be applied simultaneously to more identical devices in a parallel machines workstation. This paper presents a novel model to identify EEC policies for manufacturing systems composed of parallel and identical machines with finite buffer capacity. In this configuration, each machine can be switched on with a stochastic startup time and switched off instantaneously also considering an availability constraint. The proposed model reduces energy consumption while assuring a target availability level. The control is executed using buffer level information. Numerical results confirm model benefits when applied to a real industrial system from the automotive sector.

Reinforcement learning for energy-efficient control of multi-stage production lines with parallel machine workstations

“…Research stream for EEC of workstations with parallel machines is less developed. Loffredo et al [8] introduced a model using a Markov Decision Process (MDP) that generates effective EEC policies for a single workstation with parallel machines. Furthermore, they extended this approach with an MDP-based model for controlling multi-stage production lines with parallel machine workstations [9].…”

“…The objective of the experimental study is to assess the impact that applying the literature RL for EEC model on more parallel machines workstations has on the overall production system in the real-world industrial system (Section 3). In particular, 12 stages are controlled in the system, 𝑆𝑆 𝑖𝑖 | 𝑖𝑖 = [2,3,4,7,8,9,10,12,13,14,15,19]. The literature model is applied independently to all the 12 controllable stages in the line: there are 12 single RL agents controlling only the respective workstation observing an environment that is only a part of the overall production system.…”