Intelligent product and mechatronic software components enabling mass customisation in advanced production systems

  • Majid Sorouri
  • Valeriy Vyatkin
Special Issue Paper


Up to the present time, the control software design of production systems has been developed to produce a certain number of goods, in a centralised manner and through a case-by-case, timely and costly process. Therefore, the current control design approaches hinder factories in their pursuit to acquire the essential capabilities needed in order to survive in this customer-driven and highly competitive market. Some of these vital production competencies include mass customisation, fault tolerance reconfigurability, handling complexity, scalability and agility. The intention of this research is to propose a uniform architecture for control software design of collaborative manufacturing systems. It introduces software components named as modular, intelligent, and real-time agents (MIRAs) that represent both intelligent products as clients (C-MIRA) and machines or robots as operators (O-MIRAs) in a production system. C-MIRAs are in constant interaction with customers and operators through human machine interfaces, and are responsible for transforming products from concepts up to full realisation of them with the least possible human intervention. This architecture is built upon the IEC 61499 standard which is recognised for facilitating the distributed control design of automation systems; however, it also takes into account the intelligent product concept and envisages the machines’ control to be composed of a set of modular software components with standardised interfaces. This approach makes the software components intuitive and easy to install, to create the desired behaviour for collaborative manufacturing systems and ultimately paves the way towards mass customisation. A simplified food production case study, whose control is synthesised using the proposed approach, is chosen as an illustrative example for the proposed methodology.


IEC 61499 Mass customisation Collaborative production systems Distributed control systems Modular software components Decentralised production scheduling 


  1. 1.
    Vyatkin V et al (2006) Rapid engineering and re-configuration of automation objects aided by formal modelling and verification. Int J Manuf Res 1(4):382–404CrossRefGoogle Scholar
  2. 2.
    Koestler A (1969) The ghost in the machine. Arkana Books, LondonGoogle Scholar
  3. 3.
    Leitão P (2009) Holonic Rationale and self-organization on design of complex evolvable systems. In: Mařík V, Strasser T, Zoitl A (eds) Holonic and Multi-Agent Systems for Manufacturing. HoloMAS 2009. Lecture notes in computer science, vol 5696. Springer, Berlin, HeidelbergGoogle Scholar
  4. 4.
    Hall KH, Staron RJ, Vrba P (2005) Experience with holonic and agent-based control systems and their adoption by industry. In: Mařík V, William Brennan R, Pěchouček M (eds) Holonic and Multi-Agent Systems for Manufacturing. HoloMAS 2005. Lecture Notes in Computer Science, vol 3593. Springer, Berlin, HeidelbergGoogle Scholar
  5. 5.
    Bussmann S, Jennings NR, Wooldridge M (2004) Multiagent systems for manufacturing control: a design methodology. Springer, BerlinCrossRefGoogle Scholar
  6. 6.
    Merdan M, Lepuschitz W, Hegny I, Koppensteiner G (2009) Application of a communication interface between agents and the low level control. In: 4th. IEE International Conference on Autonomous Robots and Agents, Wellington, New Zealand, pp 628–633Google Scholar
  7. 7.
    Lepuschitz W et al (2011) Toward self-reconfiguration of manufacturing systems using automation agents. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41(1):52–69CrossRefGoogle Scholar
  8. 8.
    Li L, Yang Y (2008) Agent negotiation based ontology refinement process and mechanisms for service applications. SOCA 2(1):15–25CrossRefGoogle Scholar
  9. 9.
    Van Brussel H et al (1998) Reference architecture for holonic manufacturing systems: PROSA. Comput Ind 37(3):255–274CrossRefGoogle Scholar
  10. 10.
    Gouyon D, Pétin J-F, Morel G (2007) A product-driven reconfigurable control for shop floor systems. Studies in Informatics and Control, vol 16Google Scholar
  11. 11.
    Simao JM, Tacla CA, Stadzisz PC (2009) Holonic control metamodel. IEEE Trans Syst Man Cybern Part A Syst Hum 39(5):1126–1139CrossRefGoogle Scholar
  12. 12.
    Covanich W et al. (2007) Integrating a new machine into an existing manufacturing system by using holonic approach. In: 2007 5th IEEE international conference on industrial informatics. IEEEGoogle Scholar
  13. 13.
    McFarlane DC, Bussmann S (2000) Developments in holonic production planning and control. Prod Plan Control 11(6):522–536CrossRefGoogle Scholar
  14. 14.
    Shen W et al (2006) Applications of agent-based systems in intelligent manufacturing: an updated review. Adv Eng Inform 20(4):415–431CrossRefGoogle Scholar
  15. 15.
    Leitão P (2009) Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell 22(7):979–991MathSciNetCrossRefGoogle Scholar
  16. 16.
    Mathes M et al (2009) Time-constrained services: a framework for using real-time web services in industrial automation. SOCA 3(4):239CrossRefGoogle Scholar
  17. 17.
    Rapti E et al (2017) Decentralized service discovery and selection in Internet of Things applications based on artificial potential fields. SOCA 11(1):75–86CrossRefGoogle Scholar
  18. 18.
    Wang Z, Xu X (2012) A sharing-oriented service selection and scheduling approach for the optimization of resource utilization. SOCA 6(1):15–32CrossRefGoogle Scholar
  19. 19.
    Leitao P, Marik V, Vrba P (2012) Past, present, and future of industrial agent applications. IEEE Trans Ind Inform PP(99):1-1Google Scholar
  20. 20.
    Vyatkin V (2011) IEC 61499 as enabler of distributed and intelligent automation: state-of-the-art review. IEEE Trans Ind Inform 7(4):768–781CrossRefGoogle Scholar
  21. 21.
    Vyatkin V (2003) Intelligent mechatronic components: control system engineering using an open distributed architecture. In: Proceedings of IEEE conference on emerging technologies and factory automation, 2003. ETFA’03Google Scholar
  22. 22.
    Pang C (2012) Model-driven development of distributed automation intelligence with IEC 61499. In: Electrical and computer engineering. The University of Auckland, p 125Google Scholar
  23. 23.
    Zoitl A (2009) Real-time execution for IEC 61499. OSA & O3neidaGoogle Scholar
  24. 24.
    Sorouri M, Vyatkin V, Salcic Z (2014) MIRA: enabler of mass customization through agent-based development of intelligent manufacturing systems. In: IEEE international conference on robotics and automation (ICRA). Hong KongGoogle Scholar
  25. 25.
    Rachlin J et al (1999) A-teams: an agent architecture for optimization and decision-support. In: Müller J, Rao A, Singh M (eds) Intelligent agents V: agents theories, architectures, and languages. Springer, Berlin, pp 261–276CrossRefGoogle Scholar
  26. 26.
    Sorouri M et al (2015) Software composition and distributed operation scheduling in modular automated machines. IEEE Trans Ind Inform 11(4):865–878CrossRefGoogle Scholar
  27. 27.
    Lim MK, Zhang DZ (2004) An integrated agent-based approach for responsive control of manufacturing resources. Comput Ind Eng 46(2):221–232CrossRefGoogle Scholar
  28. 28. Accessed Mar 2013
  29. 29.
    nxtControl. (2014, 10/05). IDC Builder. Available:
  30. 30.
    Tichý P, Staron RJ (2010) Multi-agent technology for fault tolerant and flexible control. In: Srinivasan D, Jain LC (eds) Innovations in Multi-Agent Systems and Applications - 1. Studies in Computational Intelligence, vol 310. Springer, Berlin, HeidelbergGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Luleå University of TechnologyLuleåSweden
  3. 3.ITMO UniversitySt. PetersburgRussia
  4. 4.Aalto UniversityHelsinkiFinland

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