Agent-Based Control of Operational Conditions for Smart Factories: The Peak Load Management Scenario

  • Anna Florea
  • Juha Lauttamus
  • Corina Postelnicu
  • Jose L. Martinez Lastra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8062)


Operational conditions define the minimum requirements for the manufacturing site to operate while at the same time restricting the optimization potential and limiting the opportunities for reduction of resource consumption. Parameters of the manufacturing ecosystem composing these conditions depend on numerous factors. High-level intelligent control is required in order to grasp the complexity of the dependencies between the manufacturing ecosystem parameters and expand opportunities for optimization.

This paper describes an agent-based peak load management scenario and considers its implication for control architecture of smart factory operational conditions leveraging on SOA and collaborative intelligent agents paradigms.


agent-based control SOA energy efficiency peak load management 


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  1. 1.
    World Bank Data. Manufacturing, value added (% of GDP). Link: (accessed on March 02, 2013)
  2. 2.
    ICT and Energy Efficiency. The Case for Manufacturing. Recommendations of the Consultation Group. European Commission (February 2009) ISBN 978-92-79-11306-2Google Scholar
  3. 3.
    Energy Use in the New Millennium. Trends in IEA Countries. International Energy Agency (IEA) (2007)Google Scholar
  4. 4.
    Martinez Lastra, J.L., Delamer, I.M.: Semantic web services in factory automation: fundamental insights and research roadmap. IEEE Trans. Industrial Informatics, 1–11 (2006)CrossRefGoogle Scholar
  5. 5.
    Cannata, A., Gerosa, M., Taisch, M.: SOCRADES: A framework for developing intelligent systems in manufacturing. In: Transactions of IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008, pp. 1904–1908 (2008)Google Scholar
  6. 6.
    Postelnicu, C., Zhang, B., Martinez Lastra, J.L.: Embedded Service Oriented Monitoring for the Energy Aware Factory. In: 2012 6th International Conference on Information and Automation for Sustainability (2012)Google Scholar
  7. 7.
    Heilala, J., Klobut, K., Salonen, T., Siltanen, P., Ruusu, R., Armijo, A., Sorli, M., Urosevic, L., Reimer, P., Fatur, T., Gantar, Z., Jung, A.: Ambient Intelligence based monitoring and energy efficiency optimization system. In: 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM), May 25-27, pp. 1–6 (2011)Google Scholar
  8. 8.
    Cannata, A., Karnouskos, S., Taisch, M.: Energy efficiency driven process analysis and optimization in discrete manufacturing. In: 35th Annual Conference of IEEE on Industrial Electronics, IECON 2009, November 3-5, pp. 4449–4454 (2009)Google Scholar
  9. 9.
    Karnouskos, S., Colombo, A.W., Martinez Lastra, J.L., Popescu, C.: Towards the energy efficient future factory. In: 7th IEEE International Conference on Industrial Informatics, INDIN 2009, June 23-26, pp. 367–371 (2009)Google Scholar
  10. 10.
    Herrmann, C., Thiede, S.: Process chain simulation to foster energy efficiency in manufacturing. CIRP Journal of Manufacturing Science and Technology 1(4), 221–229 (2009)CrossRefGoogle Scholar
  11. 11.
    Martinez Lastra, J.L., Colombo, A.W.: Engineering framework for agent-based manufacturing control. Engineering Applications of Artificial Intelligence 19(6), 625–640 (2006)CrossRefGoogle Scholar
  12. 12.
    Leitão, P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence 22(7), 979–991 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Villaseñor Herrera, V., Vidales Ramos, A., Martínez Lastra, J.L.: An agent-based system for orchestration support of web service-enabled devices in discrete manufacturing systems. Journal of Intelligent Manufacturing 23(6), 2681–2702 (2012)CrossRefGoogle Scholar
  14. 14.
    Villaseñor Herrera, V., Bepperling, A., Lobov, A., Smit, H., Colombo, A.W., Martinez Lastra, J.L.: Integration of Multi-Agent Systems and Service-Oriented Architecture for industrial automation. In: 6th IEEE International Conference on Industrial Informatics, INDIN 2008, July 13-16, pp. 768–773 (2008)Google Scholar
  15. 15.
    Zambonelli, F., Jennings, N.R., Wooldridge, M.: Organisational Rules as an Abstraction for the Analysis and Design of Multi-Agent Systems. International Journal of Software Engineering and Knowledge Engineering 11(3), 303–328 (2001)CrossRefGoogle Scholar
  16. 16.
    Demazeau, Y., Rocha Costa, A.C.: Populations and organizations in open multi-agent systems. In: 1st National Symposium on Parallel and Distributed AI, PDAI 1996 (1996)Google Scholar
  17. 17.
    Ferber, J., Gutknecht, O.: A meta-model for the analysis and design of organizations in multi-agent systems. In: Proc. 3rd Int. Conf. on Multi-Agent Systems (ICMAS 1998). IEEE CS Press (June 1998)Google Scholar
  18. 18.
    Kendall, E.A.: Role modelling for agent system analysis, design, and implementation. In: 1st Int. Symp. on Agent Systems and Applications. IEEE CS Press (October 1999)Google Scholar
  19. 19.
    Palensky, P., Dietrich, D.: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Transactions on Industrial Informatics 7(3), 381–388 (2011)CrossRefGoogle Scholar
  20. 20.
    B2G - Smart Grids Model Region Salzburg – Building to Grid. Link: (accessed on February 20, 2013)
  21. 21.
    Sarvapali, R., Perukrishnen, V., Rogers, A., Jennings, N.: Agent-Based Control for Decentralised Demand Side Management in the Smart Grid. In: The 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, Taiwan, May 02-06, pp. 5–12 (2011)Google Scholar
  22. 22.
    Li, R., Li, J., Poulton, G.T., James, G.C.: Agent based optimisation systems for electrical load management. In: Proceedings of 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008): 1st International Workshop on Optimisation in Multiagent Systems (OptMAS 2008), Estoril, Portugal, pp. 60–69 (2008)Google Scholar
  23. 23.
    Marsh, L., Onof, C.: Stigmergic epistemology, stigmergic cognition. Cognitive Systems Research 9(1-2), 136–149 (2008)CrossRefGoogle Scholar
  24. 24.
    Li, J., Poulton, G., James, G.: Agent-Based Distributed Energy Management. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 569–578. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  25. 25.
    Xiong, G., Okuma, S., Fujita, H.: Multi-agent based experiments on uniform price and pay-as-bid electricity auction markets. In: Proceedings of the 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, DRPT 2004, vol. 1, pp. 72–76 (2004)Google Scholar
  26. 26.
    Bunn, D.W., Oliveira, F.S.: Agent-based simulation-an application to the new electricity trading arrangements of England and Wales. IEEE Transactions on Evolutionary Computation 5(5), 493–503 (2001)CrossRefGoogle Scholar
  27. 27.
    Puttonen, J., Lobov, A., Cavia Soto, M.A., Martinez Lastra, J.L.: A Semantic Web Services-based approach for production systems control. Advanced Engineering Informatics 24(3), 285–299 (2010)CrossRefGoogle Scholar
  28. 28.
    Jade - Java Agent DEvelopment Framework. Link: (accessed on February 20, 2013)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anna Florea
    • 1
  • Juha Lauttamus
    • 1
  • Corina Postelnicu
    • 1
  • Jose L. Martinez Lastra
    • 1
  1. 1.Tampere University of TechnologyTampereFinland

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