Agent-Oriented Smart Factory (AOSF): An MAS Based Framework for SMEs Under Industry 4.0

  • Fareed Ud DinEmail author
  • Frans Henskens
  • David Paul
  • Mark Wallis
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 96)


For the concept of Industry 4.0 to come true, a mature amalgamation of allied technologies is obligatory, i.e. Internet of Things (IoT), Big Data analytics, Mobile Computing, Multi-Agent Systems (MAS) and Cloud Computing. With the emergence of the fourth industrial revolution, proliferation in the field of Cyber-Physical Systems (CPS) and Smart Factory gave a boost to recent research in this dimension. Despite many autonomous frameworks contributed in this area, there are very few widely acceptable implementation frameworks, particularly for Small to Medium Size Enterprises (SMEs) under the umbrella of Industry 4.0. This paper presents an Agent-Oriented Smart Factory (AOSF) framework, integrating the whole supply chain (SC), from supplier-end to customer-end. The AOSF framework presents an elegant mediating mechanism between multiple agents to increase robustness in decision making at the base level. Classification of agents, negotiation mechanism and few results from a test case are presented.


Smart factory Multi-Agent Systems (MAS) Cyber-Physical Systems (CPS) Small to Medium Size Enterprises (SMEs) 


  1. 1.
    Deane, P.M.: The First Industrial Revolution. Cambridge University Press, New York (1979)Google Scholar
  2. 2.
    Mokyr, J.: The second industrial revolution, 1870–1914. In: Storia dell’economia Mondiale, pp. 219–245 (1998)Google Scholar
  3. 3.
    Freeman, C., Louçã, F.: As Time Goes By: From the Industrial Revolutions to the Information Revolution. Oxford University Press, Oxford (2001)Google Scholar
  4. 4.
    Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M.: How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int. J. Mech. Ind. Sci. Eng. 8(1), 37–44 (2014)Google Scholar
  5. 5.
    Wang, S., Wan, J., Li, D., Zhang, C.: Implementing smart factory of industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12(1) (2016). Scholar
  6. 6.
    Lee, J., Bagheri, B., Kao, H.-A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3(Suppl. C), 18–23 (2015)CrossRefGoogle Scholar
  7. 7.
    He, W., Da Xu, L.: Integration of distributed enterprise applications: a survey. IEEE Trans. Ind. Inform. 10(1), 35–42 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Majeed, A.A., Rupasinghe, T.D.: Internet of Things (IoT) embedded future supply chains for industry 4.0: an assessment from an ERP-based fashion apparel and footwear industry. Int. J. Supply Chain Manag. 6(1), 25–40 (2017)Google Scholar
  9. 9.
    Manogaran, G., Thota, C., Lopez, D., Sundarasekar, R.: Big data security intelligence for healthcare industry 4.0. In: Cybersecurity for Industry 4.0, pp. 103–126. Springer (2017)Google Scholar
  10. 10.
    Adeyeri, M.K., Mpofu, K., Olukorede, T.A.: Integration of agent technology into manufacturing enterprise: a review and platform for industry 4.0. In: International Conference on Industrial Engineering and Operations Management (IEOM), pp. 1–10. IEEE (2015)Google Scholar
  11. 11.
    Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., Ivanova, M.: A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int. J. Prod. Res. 54(2), 386–402 (2016)CrossRefGoogle Scholar
  12. 12.
    Voss, S., Sebastian, H.-J., Pahl, J.: Introduction to intelligent decision support and big data for logistics and supply chain management minitrack (2017)Google Scholar
  13. 13.
    Shen, W., Hao, Q., Yoon, H.J., Norrie, D.H.: Applications of agent-based systems in intelligent manufacturing: an updated review. Adv. Eng. INFORM. 20(4), 415–431 (2006)CrossRefGoogle Scholar
  14. 14.
    Sadeh, N.M., Hildum, D.W., Kjenstad, D.: Agent-based E-supply chain decision support. J. Organ. Comput. Electron. Commer. 13(3–4), 225–241 (2003)Google Scholar
  15. 15.
    Shen, W.: Distributed manufacturing scheduling using intelligent agents. IEEE Intell. Syst. 17(1), 88–94 (2002)CrossRefGoogle Scholar
  16. 16.
    Shen, W.: Genetic algorithms in agent-based manufacturing scheduling systems. Integr. Comput. Aided Eng. 9(3), 207–217 (2002)Google Scholar
  17. 17.
    Richards, G.: Warehouse Management: A Complete Guide to Improving Efficiency and Minimizing Costs in the Modern Warehouse. Kogan Page Publishers, London (2017)Google Scholar
  18. 18.
    De Koster, R.B., Johnson, A.L., Roy, D.: Warehouse design and management (2017)Google Scholar
  19. 19.
    Centobelli, P., Converso, G., Murino, T., Santillo, L.: Flow shop scheduling algorithm to optimize warehouse activities. Int. J. Ind. Eng. Comput. 7(1), 49–66 (2016)Google Scholar
  20. 20.
    Ma, H., Su, S., Simon, D., Fei, M.: Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling. Eng. Appl. Artif. Intell. 44, 79–90 (2015)CrossRefGoogle Scholar
  21. 21.
    Manzini, R., Accorsi, R., Baruffaldi, G., Cennerazzo, T., Gamberi, M.: Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS). Int. J. Prod. Res. 54(14), 4286–4304 (2016)CrossRefGoogle Scholar
  22. 22.
    Llonch, M., Bernardo, M., Presas, P.: A case study of a simultaneous integration in an SME: implementation process and cost analysis. Int. J. Qual. Reliab. Manag. 35, 319–334 (2018)CrossRefGoogle Scholar
  23. 23.
    Kishore, R., Zhang, H., Ramesh, R.: Enterprise integration using the agent paradigm: foundations of multi-agent-based integrative business information systems. Dec. Support Syst. 42(1), 48–78 (2006)CrossRefGoogle Scholar
  24. 24.
    Ud Din, F., Anwer, S.: ERP success and logistical performance indicators a critical view. Int. J. Comput. Sci. Issues, 223–229 (2013).
  25. 25.
    Ruta, M., Scioscia, F., Di Noia, T., Di Sciascio, E.: Reasoning in pervasive environments: an implementation of concept abduction with mobile OODBMS. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT 2009, vol. 1, pp. 145–148. IEEE (2009)Google Scholar
  26. 26.
    Loseto, G., Scioscia, F., Ruta, M., Di Sciascio, E.: Semantic-based smart homes: a multi-agent approach. In: WOA (2012)Google Scholar
  27. 27.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Artificial Intelligence, p. 27. Prentice-Hall, Englewood Cliffs (1995)zbMATHGoogle Scholar
  28. 28.
    Minglei, L., Hongwei, W., Chao, Q.: A novel HTN planning approach for handling disruption during plan execution. Appl. Intell. 46(4), 800–809 (2017)CrossRefGoogle Scholar
  29. 29.
    Strobel, V., Kirsch, A.: Planning in the wild: modeling tools for PDDL. In: Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz), pp. 273–284. Springer (2014)Google Scholar
  30. 30.
    Lemai-Chenevier, S.: IXTET-EXEC: planning, plan repair and execution control with time and resource management, Ph.D. thesis (2004)Google Scholar
  31. 31.
    Piasecki, D.: “Warehouse management systems (WMS),” Inventory Operations Consulting LLC (2005).
  32. 32.
    Jones, M.M., Juneja, M.O., Gnanamurthy, K., Kandikuppa, K., Sheu, J.Y.W., William, E.R.V., Hadagali, G.R., Rawat, S.S., Berry, V., Agrawal, D., et al.: Consigned inventory management system, 31 March 2016. US Patent App. 14/499,372 (2016)Google Scholar
  33. 33.
    Preuveneers, D., Berbers, Y.: Modeling human actors in an intelligent automated warehouse. In: International Conference on Digital Human Modeling, pp. 285–294. Springer (2009)CrossRefGoogle Scholar
  34. 34.
    JaCaMo, C.: Framework for Jason and Moise, “Jacamo framework for jason, cartago and moise.” (2017).
  35. 35.
    Cardoso, R.C., Bordini, R.H.: A distributed online multi-agent planning system. In: Distributed and Multi-Agent Planning (DMAP-2016), p. 15 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Fareed Ud Din
    • 1
    Email author
  • Frans Henskens
    • 1
  • David Paul
    • 2
  • Mark Wallis
    • 1
  1. 1.University of NewcastleCallaghanAustralia
  2. 2.University of New EnglandArmidaleAustralia

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