Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.
L'industrie 4.0 s'accompagne de la prise en compte de contraintes de développement durable. Dans ce contexte, nous proposons une architecture multi-agent pour l'ordonnancement prédictif et réactif coordonné entre des systèmes de production de biens et des systèmes de production d'énergie renouvelable, appelée EasySched. La validation de cette architecture est originale, elle est menée de manière complètement et physiquement distribuée en utilisant des systèmes embarqués en réseau. Cette validation est menée sur une série d'instances inspirées de la littérature. Les résultats montrent que les mécanismes proposés permettent d'adapter la production selon l'énergie renouvelable disponible.
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