Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

Maroua Nouiri, Damien Trentesaux and Abdelghani Bekrar
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Maroua Nouiri: LAMIH, UMR CNRS 8201, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France
Damien Trentesaux: LAMIH, UMR CNRS 8201, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France
Abdelghani Bekrar: LAMIH, UMR CNRS 8201, Université Polytechnique Hauts-de-France, 59313 Valenciennes, France

Energies, 2019, vol. 12, issue 23, 1-30

Abstract: 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.

Keywords: predictive scheduling; reactive scheduling; energy efficiency; multi-agent modelling; cyber physical production system; cyber physical energy system; renewable energies; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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