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Normative agent-based simulation for supply chain planning

Author

Listed:
  • L Ferreira

    (Federal University of Rio Grande do Norte)

  • D Borenstein

    (Federal University of Rio Grande do Sul)

Abstract

This paper presents an agent-based simulation framework for supply chain (SC) planning, introducing the notion of normative agent. The analysis of the relevant literature shows that most research works carried out in this area aim to handle specific problems and contexts. Although some methodologies and more generic solutions have been proposed, they are not able to cope with SCs in which regulation plays an important role, whether issued by a government agent or by an international institution. Several SCs, such as in the energy, food, chemical, and forestry areas, are highly regulated. Explicitly modelling the actors involved in the regulation of SCs using normative agents allowed us to evaluate the potential benefits of alternative strategies for planning of regulated SCs. The modelling of a biodiesel SC is presented as a case study.

Suggested Citation

  • L Ferreira & D Borenstein, 2011. "Normative agent-based simulation for supply chain planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 501-514, March.
  • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:3:d:10.1057_jors.2010.144
    DOI: 10.1057/jors.2010.144
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    References listed on IDEAS

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    1. Hung, Wing Yan & Samsatli, Nouri J. & Shah, Nilay, 2006. "Object-oriented dynamic supply-chain modelling incorporated with production scheduling," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1064-1076, March.
    2. Sabri, Ehap H. & Beamon, Benita M., 2000. "A multi-objective approach to simultaneous strategic and operational planning in supply chain design," Omega, Elsevier, vol. 28(5), pages 581-598, October.
    3. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    4. Guido Boella & Leendert Torre & Harko Verhagen, 2006. "Introduction to normative multiagent systems," Computational and Mathematical Organization Theory, Springer, vol. 12(2), pages 71-79, October.
    5. X Chen & F B Zhan, 2008. "Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(1), pages 25-33, January.
    6. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
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    Cited by:

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