An autonomous multi-agent approach to supply chain event management

Lorena A. Bearzotti, Enrique Salomone, Omar J. Chiotti

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Organizations have made a significant effort to implement software for planning and scheduling, but disruptive event management is still a problem to be solved. Since a disruptive event can affect the overall performance of the supply chain, SCEM (Supply Chain Event Management) systems presenting different automation levels such as monitoring, alarm and decision support have been proposed. However, the management of disruptive events, taking into account the distributed nature of the supply chain, the members autonomy and the ability to exert corrective control actions, has been identified as a problem that requires further research. This work presents an agent-based approach for the SCEM problem, which can perform autonomous corrective control actions to minimize the effect of deviations in the plan that is currently being executed. These control actions consist of a distribution of the variation between supply chain members, using the plans slack in a collaborative way. An innovative feature of this approach is its focus on resources, which are affected by disruptive events in a direct way. Based on this approach, a SCEM system is designed as a net of control points defined on resources connected through supply process orders. Two novel aspects are the distributed collaborative inter-organizational architecture of the SCEM system and a Double Contract Net Protocol. This protocol allows a set of resource:representing agents to interact through an agent, representing a supply process order as a mediator. An application to a case study of the Multi-Agent SCEM system implemented with JADE is provided.

Original languageEnglish
Pages (from-to)468-478
Number of pages11
JournalInternational Journal of Production Economics
Volume135
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

Keywords

  • Autonomous behavior
  • Event management
  • Multi-agent system
  • Supply chain

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