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Disruption management in distributed enterprises: A multi-agent modelling and simulation of cooperative recovery behaviours

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  • Cauvin, A.C.A.
  • Ferrarini, A.F.A.
  • Tranvouez, E.T.E.

Abstract

Disruption management in industrial areas consists in dealing with unanticipated events that get the plans deviate from their intended course. The solution results from the design and the maintenance of an operating mode ensuring a relevant deployment of individual recovery behaviours. The paper proposes an approach to minimize the impact of disrupting events on the whole system. It is based on an analysis of disrupting events and the characterization of the recovery process, and on a cooperative repair method for distributed industrial systems. This method is based on a cooperative distributed problem solving approach supported by a multi-agent system framework.

Suggested Citation

  • Cauvin, A.C.A. & Ferrarini, A.F.A. & Tranvouez, E.T.E., 2009. "Disruption management in distributed enterprises: A multi-agent modelling and simulation of cooperative recovery behaviours," International Journal of Production Economics, Elsevier, vol. 122(1), pages 429-439, November.
    • Handle: RePEc:eee:proeco:v:122:y:2009:i:1:p:429-439
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    References listed on IDEAS

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    Cited by:

    1. Ge, Houtian & Gray, Richard & Nolan, James, 2015. "Agricultural supply chain optimization and complexity: A comparison of analytic vs simulated solutions and policies," International Journal of Production Economics, Elsevier, vol. 159(C), pages 208-220.
    2. Guarnaschelli, Armando & Chiotti, Omar & Salomone, Hector E., 2013. "An approach based on constraint satisfaction problems to disruptive event management in supply chains," International Journal of Production Economics, Elsevier, vol. 144(1), pages 223-242.
    3. Wang, Xuping & Ruan, Junhu & Shi, Yan, 2012. "A recovery model for combinational disruptions in logistics delivery: Considering the real-world participators," International Journal of Production Economics, Elsevier, vol. 140(1), pages 508-520.
    4. Bearzotti, Lorena A. & Salomone, Enrique & Chiotti, Omar J., 2012. "An autonomous multi-agent approach to supply chain event management," International Journal of Production Economics, Elsevier, vol. 135(1), pages 468-478.

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