The Holonic Production System: A Multi Agent Simulation Approach
Gandolfo Dominici, Pierluigi Argoneto, Paolo Renna, Luigi Cuccia
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DOI: 10.4236/ib.2010.23025   PDF    HTML     5,357 Downloads   9,772 Views   Citations

Abstract

Today’s turbulent markets are facing unpredictable and sudden variations in demand. In this context, the Holonic Production System (HPS) seems to be able to overcome the operational and economic problems of traditional production systems. The HPS’ ability to adapt and react to business environment changes, whilst maintaining systemic synergies and coordination, leverage on its network organizational structure, assuring both flexibility and profitability. In this paper we study HPS experimentally, modeling holon-firms as agents. In our simulation, holon-firms interact both with each other and with the external environment without predetermined hierarchies and following their own aims and internal decision rules with a negotiation-based control system. The Multi Agent System Approach we propose aims to evaluate and test the performance of the HPS to adjust to changes in market demand by simulating variations in holon-firms’ capacity and reconfiguration costs in real time in a distributed enterprise network. Hence we demonstrate that, through a collaborative negotiation approach, the HPS results in a better adaptability and improved network responsiveness.

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G. Dominici, P. Argoneto, P. Renna and L. Cuccia, "The Holonic Production System: A Multi Agent Simulation Approach," iBusiness, Vol. 2 No. 3, 2010, pp. 201-209. doi: 10.4236/ib.2010.23025.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] G. Dominici, “Demand Driven Supply Chain ed Innova- zione: Il Sistema Logistico-Produttivo per la Soddisfazi- one dei Bisogni del Cliente,” In: A. Purpura and G. Fazio, Eds., Economia e Gestione Dell’innovazione nelle PMI, FrancoAngeli, Milan, 2008.
[2] G. Dominici, “Holonic Production System to Obtain Flexibility for Customer Satisfaction,” Journal of Service Science & Management, Vol. 1, No. 3, 2008, pp. 251-254.
[3] F. Frederix, “From Production to a Product Perspective. New Industrial Scenario?” In: S. Yoon, et al., Evolution of Supply Chain Management, Symbiosis of Adaptive Value Networks and ICT, Kluwer Academic Publishers, Norwell, 2004.
[4] L. Gou, P. B. Luh and Y. Kyoya, “Holonic Manufac- turing Scheduling: Architecture, Cooperation Mechanism, and Implementation,” Computers in Industry, Vol. 37, No. 3, 1998, pp. 213-231.
[5] J. Hatvany, “Intelligence and Cooperation in Heterarchic Manufacturing Systems,” Robotics & Computer-Integrat- ed Manufacturing, Vol. 2, No. 2, 1985, pp. 101-104.
[6] H. van Brussel, L. Bongaerts, J. Wyns, P. Valckenaers and T. van Ginderachter, “A Conceptual Framework for Holonic Manufacturing: Identification of Manufacturing Holons,” Journal of Manufacturing Systems, Vol. 18, No. 1, 1999, pp. 35-52.
[7] P. Valckenaers, E. Bonneville, H. van Brussel, L. Bongaerts and J. Wyns, “Results of the Holonic Control System Benchmark at the KULeven,” Proceedings of Computer Integrated Manufacturing and Automation Technology Conference, Troy, New York, 1994, pp. 128-133.
[8] T. Moyaux, B. Chaib-draa and S. D’Amours, “Supply Chain Management and Multiagent Systems: An Overview,” In: B. Chaib-draa and J. P. Müller, Eds., Multiagent Based Supply Chain Management, Springer, Ontario, 2006.
[9] M. Paolucci and R. Sacile, “Agent-Based Manufacturing and Control Systems,” CRC Press, Boca Raton, 2005.
[10] G. M. Golinelli, “Viable System Approach (VSA),” Community Economic Development Association of Michigan, Michigan, 2010.
[11] A. Koestler, “The Ghost in the Machine,” Arkana, London, 1967.
[12] C. Saccani, “Il Sistema Olonico,” Sistemi & Impresa, Vol. 2, 1996, pp. 29-45.
[13] M. A. Arbib, “The Handbook of Brain Theory and Neural Networks,” MIT Press, Boston, 1995.
[14] P. Renna and P. Argoneto, “Capacity Allocation in Multi- Site Environment: A Multi Agent Systems Approach,” In: Proceedings of Intelligent Computation in Manufacturing Engineering, Naples, 2008.

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