Multi-behavior agent model for planning in supply chains: An application to the lumber industry

  • Authors:

  • Pascal Forget;Sophie D'Amours;Jean-Marc Frayret

  • Affiliations:

  • FOR@C Research Consortium, Pavillon Adrien-Pouliot, Université Laval, Quebec (QC), Canada G1K 7P4;FOR@C Research Consortium, Pavillon Adrien-Pouliot, Université Laval, Quebec (QC), Canada G1K 7P4;Mathematics and Industrial Engineering Department, ícole Polytechnique de Montréal, 2500 chemin de Polytechnique, Montreal (QC), Canada H3T 1J4

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2008

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Abstract

Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for planning systems to present planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, agent-based technology takes advantage of the ability of agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, planning agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior planning agent model using different planning strategies when decisions are supported by a distributed planning system. The implementation of this solution is realized through the FOR@C experimental agent-based platform, dedicated to supply chain planning for the lumber industry.