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Normative agent-based simulation for supply chain planning

  • Theoretical Papers Supply Chain Planning
  • Published:
Journal of the Operational Research Society

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.

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References

  • Allwood JM and Lee JH (2005). The design of an agent for modeling supply chain network dynamics. Int J Prod Res 43: 4875–4898.

    Article  Google Scholar 

  • Beamon BM (1998). Supply chain design and analysis: Models and methods. Int J Prod Econ 55: 281–294.

    Article  Google Scholar 

  • Boella G, van der Torre L and Verhagen H (2006). Introduction to normative multiagent systems. Comput Math Organ Theory 12: 71–79.

    Article  Google Scholar 

  • Cavalieri S, Cesarotti V and Introna V (2003). A multiagent model for coordinated distribution chain planning. J Org Comp Elect Com 13: 267–287.

    Google Scholar 

  • Chatfield DC, Hayya JC and Harrison TP (2007). A multi-formalism architecture for agent-based, order-centric supply chain simulation. Sim Model Pract Th 15: 153–174.

    Article  Google Scholar 

  • Chen X and Zhan FB (2008). Agent-based modeling and simulation of urban evacuation: Relative effectiveness of simultaneous and staged evacuation strategies. J Opl Res Soc 59: 28–53.

    Google Scholar 

  • Chiu M and Lin G (2004). Collaborative supply chain planning using the artificial neural network approach. J Manuf Tech Mngt 15: 787–796.

    Article  Google Scholar 

  • Clarke G and Wright JW (1964). Scheduling of vehicles from a central depot to a number of delivery points. Opns Res 12: 568–581.

    Article  Google Scholar 

  • Davidsson P and Kwernstedt F (2002). A multi-agent system architecture for coordination of just-in-time production and distribution. Knowl Eng Rev 17: 317–329.

    Article  Google Scholar 

  • Fox MS, Barbuceanu M and Teigen R (2000). Agent-oriented supply chain management. Int J Flex Manuf Syst 12: 165–188.

    Article  Google Scholar 

  • Frayret JM, D'Amours S, Rousseau A, Harvey S and Gaudreault J (2007). Agent-based supply-chain planning in the forest products industry. Int J Flex Manuf Syst 19: 358–391.

    Article  Google Scholar 

  • Govindu R and Chinnam RB (2007). MASCF: A generic process-centered methodological framework for analysis and design of multi-agent supply chain systems. Comput Ind Eng 53: 584–609.

    Article  Google Scholar 

  • Gunasekaran A, Macbeth DK and Lamming R (2000). Modeling and analysis of supply chain management systems. J Opl Res Soc 51: 1112–1115.

    Article  Google Scholar 

  • Hass MJ, McAloon AJ, Yee WC and Foglia TA (2006). A process model to estimate biodiesel production costs. Bioresource Technol 97: 671–678.

    Article  Google Scholar 

  • Hung WY, Samsatli NJ and Shah N (2006). Object-oriented dynamic supply-chain modeling incorporated with production scheduling. Eur J Opl Res 169: 1064–1076.

    Article  Google Scholar 

  • Janssen M (2005). The architecture and business value of a semi-cooperative, agent-based supply chain management system. Electron Comme R A 4: 315–328.

    Article  Google Scholar 

  • Julka N, Srinivasan R and Karimi I (2002). Agent-based supply chain management—1: Framework. Comput Chem Eng 26: 1755–1769.

    Article  Google Scholar 

  • Keeney RL and Raiffa H (1976). Decision with Multiple Objectives: Preferences and value Trade-Offs. Wiley: New York.

    Google Scholar 

  • Lai CL, Lee WB and IP WH (2003). A study of system dynamics in just-in-time logistics. J Mater Process Tech 138: 265–269.

    Article  Google Scholar 

  • Lim SJ, Suk JJ, Kim KS and Park MW (2006). A simulation approach for production-distribution planning with consideration given to replenishment policies. Int J Adv Manuf Tech 27: 593–603.

    Article  Google Scholar 

  • López F, Luck M and d'Inverno M (2005). A normative framework for agent-based systems. In: Boella G, Torre L and Verhagen H (eds) Proceedings of the Symposium on Normative Multiagent Systems. Society for the Study of Artificial Intelligence and the Simulation of Behaviour: Hatfield, England, pp 24–35.

  • Macal CM and North MJ (2005). Tutorial on agent-based modeling and simulation. In: Kuhl ME, Steiger NM, Armstrong FB and Joines JA (eds). Proceedings of the 2005 Winter Simulation Conference. IEEE: Piscataway, NJ, pp 2–15.

  • Mele FD, Guillén G, Espuña A and Puigjaner L (2007). An agent-based approach for supply chain retrofitting under uncertainty. Comput Chem Eng 31: 722–735.

    Article  Google Scholar 

  • Monostori L, Vancza J and Kumara SRT (2006). Agent-based systems for manufacturing. CIRP Ann Manuf Techn 55: 697–720.

    Article  Google Scholar 

  • Ng WK, Pipiane R and Viswanathan S (2003). Simulation workbench for analysing multi-echelon supply chains. Integr Manuf Syst 14: 449–457.

    Article  Google Scholar 

  • Sabri EH and Beamon BM (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega—Int J Mngt 28: 581–598.

    Google Scholar 

  • Schieritz N and Größler A (2002). Emergent structures in supply chains—A study integrating agent-based and system dynamics modeling. In: Sprague R (ed.). Proceedings of the 36th Hawaii International Conference on System Sciences. IEEE Computer Society: Silver Spring, MD, p 94a.

    Google Scholar 

  • Shen W, Hao Q, Yoon HJ and Norrie DH (2006). Applications of agent-based systems in intelligent manufacturing: An updated review. Adv Eng Inform 20: 415–431.

    Article  Google Scholar 

  • Simchi-Levi D, Kaminsky P and Simchi-Levi E (2003). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. McGraw-Hill: New York.

    Google Scholar 

  • Swaminathan JM, Smith SF and Sadeh NM (1998). Modeling supply chain dynamics: A multiagent approach. Decis Sci 29: 607–632.

    Article  Google Scholar 

  • Valluri A and Croson DC (2005). Agent learning in supplier selection models. Decis Support Syst 39: 219–240.

    Article  Google Scholar 

  • van der Vorst JGAJ, Beules AJM and van Beck P (2001). Modeling and simulating multi-echelon food systems. Eur J Opl Res 122: 354–366.

    Article  Google Scholar 

  • van der Zee DJ and van der Vorst JGAJ (2005). A modeling framework for supply chain simulation: Opportunities for improved decision making. Decis Sci 36: 65–95.

    Article  Google Scholar 

  • Wang Y and Fang L (2007). Design of an intelligent agent-based supply chain simulation system. In: El-Hawary M (ed.). IEEE International Conference on Systems, Man and Cybernetics. IEEE Systems: New York, p 1836.

    Google Scholar 

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Ferreira, L., Borenstein, D. Normative agent-based simulation for supply chain planning. J Oper Res Soc 62, 501–514 (2011). https://doi.org/10.1057/jors.2010.144

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  • DOI: https://doi.org/10.1057/jors.2010.144

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