MODELO DE UN SISTEMA MULTI-AGENTE PARA LA OPTIMIZACIÓN DE LA CADENA DE SUMINISTROS EN LA INDUSTRIA DE LA MADERA DE CONÍFERAS MUTI-AGENT SYSTEM MODEL FOR THE OPTIMIZATION OF SOFTWOOD INDUSTRY SUPPLY CHAIN

The lumber industry sector forms a productive chain that be summarized in the three processes: sawing, drying and remanufacturing. Where satisfying the requested volume and meet the planned delivery dates is important to both, the client and each production chain. The non-integration presents coherence issues in the production plans. Hence, an integrated model in lumber sector, that uses the working advantages of multi-agents systems, works to improve coherence and the system’s global productivity. The integration model presented in this paper minimizes global tardiness of the lumber transformation chain, at the moment of delivering production orders. Within the system, the communication between agents is done by using the Contract Net Protocol. The results show the sawmill, as a first step in the system, is where the biggest tardiness and bottle neck in the fulfilling of the production orders is produced. In average, the results showed a tardiness between 2 to 4 days in the compliance of chain production orders. The maximum delay is of 20 days.

[1]  Sophie D'Amours,et al.  Agent-Based Supply Chain Planning in the Forest Products Industry , 2006, BASYS.

[2]  Honorio F. Carino,et al.  Enhancing the profitability of a vertically integrated wood products production system. Part 1. A multistage modelling approach , 2001 .

[3]  Jean-Marc Frayret,et al.  Agent-based Simulation for Distributed Supply Chain Planning: Conceptual Modeling, Analysis and Illustration , 2007 .

[4]  Nicholas R. Jennings On Agent-Based Software Engineering" Artificial Intelligence , 2000 .

[5]  Michael N. Huhns,et al.  Multiagent systems and societies of agents , 1999 .

[6]  Benoit Montreuil,et al.  Coordination and control in distributed and agent-based manufacturing systems , 2004 .

[7]  Thomas C. Maness,et al.  The Combined Optimization of Log Bucking and Sawing Strategies , 2007 .

[8]  Philip A. Araman,et al.  Combining Simulation and Optimization Models for Hardwood Lumber Production , 1991 .

[9]  Brahim Chaib-draa,et al.  Multi-Agent coordination based on tokens: reduction of the bullwhip effect in a forest supply chain , 2003, AAMAS '03.

[10]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[11]  Patricio Donoso,et al.  Internal supply chain management in the Chilean sawmill industry , 2007 .

[12]  Ramón Companys Pascual,et al.  Estado del arte de la planificación colaborativa en la cadena de suministro: contexto determinista e incierto , 2007 .

[13]  Thomas C. Maness,et al.  Multiple Period Combined Optimization Approach to Forest Production Planning , 2002 .

[14]  Paul Levi,et al.  Distributed Negotiation-Based Task Planning for a Flexible Manufacturing Environment , 1994, MAAMAW.

[15]  Thomas C. Maness,et al.  Production planning for integrated primary and secondary lumber manufacturing , 2007 .

[16]  Patrick Charpentier,et al.  From a reactive, heterarchical to a holonic system: an application for optimizing flow in an automotive plant , 2004 .

[17]  Thomas C. Maness,et al.  A relational database approach to a linear programming-based decision support system for production planning in secondary wood product manufacturing , 2005, Decis. Support Syst..

[18]  Felipe F. Baesler,et al.  PROGRAMACIÓN MULTIOBJETIVO DE MÁQUINAS MOLDURERAS A TRAVÉS DE ALGORITMOS MEMÉTICOS MULTIOBJECTIVE MOLDING MACHINE SCHEDULING USING MEMETIC ALGORITHMS , 2006 .

[19]  Thierry Moyaux,et al.  Supply Chain Management and Multiagent Systems: An Overview , 2006 .

[20]  Neil A. Duffie,et al.  Real-time distributed scheduling of heterarchical manufacturing systems , 1994 .

[21]  Hartmut Stadtler,et al.  Negotiation-based collaborative planning between supply chains partners , 2005, Eur. J. Oper. Res..

[22]  Astghik Babayan,et al.  Solving the n-job 3-stage flexible flowshop scheduling problem using an agent-based approach , 2004 .