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Automation platform independent multi-agent system for robust networks of production resources in industry 4.0

Author

Listed:
  • Matthias Seitz

    (Technical University of Munich)

  • Felix Gehlhoff

    (Helmut Schmidt University)

  • Luis Alberto Cruz Salazar

    (Technical University of Munich
    Universidad Antonio Nariño)

  • Alexander Fay

    (Helmut Schmidt University)

  • Birgit Vogel-Heuser

    (Technical University of Munich)

Abstract

The Cyber-Physical Production System (CPPS) is a concept derived from software (cyber) and hardware (physical) applications and is based on global information exchange between such systems. The CPPS is known as a trend of Industry 4.0 (I4.0) focusing on flexibility regarding new products and adaptability to new requirements. This paper focuses on two I4.0 scenarios described by the Platform Industrie 4.0 that describe challenges for the industry towards its digital future. First, it looks at the Order Controlled Production (OCP) scenario that deals with flexible and self-configuring production networks. It describes the dynamic organization of production resources required to execute a production order. Second, the Adaptable Factory (AF) application scenario is discussed, which focuses on the configuration of production resources and describes the adaptability of an individual facility through (physical) modification. This paper first provides a detailed analysis of the requirements from these scenarios. Furthermore, it analyses the current Multi-Agent System (MAS) architectures and agent-based planning and decision support systems requirements. MAS can be used to create application-independent I4.0 systems with arbitrary hardware automation platforms. To create a scalable communication network that also supports application independence and enables the semantically machine-readable description of the exchanged data, the OPC UA standard was adopted. As a result of the study, the concept shows how different and independent automation platforms can be seamlessly connected via OPC UA. The proposed MAS concept has been evaluated in different use cases, namely OCP and AF.

Suggested Citation

  • Matthias Seitz & Felix Gehlhoff & Luis Alberto Cruz Salazar & Alexander Fay & Birgit Vogel-Heuser, 2021. "Automation platform independent multi-agent system for robust networks of production resources in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2023-2041, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-021-01759-2
    DOI: 10.1007/s10845-021-01759-2
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    References listed on IDEAS

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    1. Adrià Salvador Palau & Maharshi Harshadbhai Dhada & Ajith Kumar Parlikad, 2019. "Multi-agent system architectures for collaborative prognostics," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2999-3013, December.
    2. Traci J. Hess & Loren Paul Rees & Terry R. Rakes, 2008. "Using Autonomous Software Agents in Decision Support Systems," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 25, pages 529-555, Springer.
    3. Luis Ribeiro & Martin Hochwallner, 2018. "On the Design Complexity of Cyberphysical Production Systems," Complexity, Hindawi, vol. 2018, pages 1-13, June.
    4. Poorya Ghafoorpoor Yazdi & Aydin Azizi & Majid Hashemipour, 2018. "An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach," Sustainability, MDPI, vol. 10(9), pages 1-28, August.
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    Cited by:

    1. Edgar Chacón & Luis Alberto Cruz Salazar & Juan Cardillo & Yenny Alexandra Paredes Astudillo, 2021. "A control architecture for continuous production processes based on industry 4.0: water supply systems application," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2061-2081, October.
    2. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.

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