Impacto da Confiança em Simulação Baseada em Agentes para Cadeias de Suprimentos
Resumo
Empresas em cadeias de suprimentos tem como objetivo maximizar suas produtividades, e consequentemente seus lucros. Uma maneira de estudar o comportamento destas cadeias é simulá-las utilizando sistemas multi-agentes; um modelo adotado na literatura é o Beer Game. Neste trabalho, adicionamos múltiplos agentes nos níveis do Beer Game para avaliar a eficiência local e global dos fornecedores, utilizando perfis baseados em confiança ou em preço. Medimos o impacto de usar tal política de seleção baseada em confiança no lucro dos agentes.Referências
Akkermans, H. (2001). Emergent supply networks: system dynamics simulation of adaptive supply agents. In Proceedings of the 34th Annual Hawaii International Conference on System Sciences, page 11. IEEE Comput. Soc.
Collier, N. (2003). RePast : An Extensible Framework for Agent Simulation. The University of Chicagos Social Science Research, 36:371–375.
Croom, S., Romano, P., and Giannakis, M. (2000). Supply chain management: an analytical framework for critical literature review. European journal of purchasing & supply management, 6(1):67–83.
De La Fuente, D. and Lozano, J. (2007). Application of distributed intelligence to reduce the bullwhip effect. International Journal of Production Research, 45(8):1815–1833.
Eclipse, I. (2007). Eclipse foundation.
Edali, M. and Yasarcanb, H. (2014). A Mathematical Model of the Beer Game. JASSS.
Forrester, J. W. (1997). Industrial dynamics. Journal of the Operational Research Society, 48(10):1037–1041.
Friedman, M. (2007). The social responsibility of business is to increase its profits. Corporate ethics and corporate governance, pages 173–178.
Handfield, R. B. and Bechtel, C. (2002). The role of trust and relationship structure in improving supply chain responsiveness. Industrial marketing management, 31(4):367–382.
Hou, Y., Xiong, Y., Wang, X., and Liang, X. (2014). The effects of a trust mechanism on a dynamic supply chain network. Expert Systems with Applications, 41(6):3060–3068.
Kim, W. S. (2009). Effects of a trust mechanism on complex adaptive supply networks: An agent-based social simulation study. JASSS, 12(3).
Lambert, D. M. and Cooper, M. C. (2000). Issues in supply chain management. Industrial marketing management, 29(1):65–83.
Lee, H. L., Padmanabhan, V., and Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect. Source: Management Science, 43(4):546–558.
Lin, F.-r., Sung, Y.-W., and Lo, Y.-P. (2005). Effects of trust mechanisms on supply-chain performance: A multi-agent simulation study. International Journal of Electronic Commerce, 9(4):9–112.
Mayer, R. C., Davis, J. H., and Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3):709–734.
Nagurney, A. (2006). Supply chain network economics: dynamics of prices, flows and profits. Edward Elgar Publishing.
Ozik, J., Collier, N. T., Murphy, J. T., and North, M. J. (2013). The relogo agent-based modeling language. In Simulation Conference (WSC), 2013 Winter, pages 1560–1568. IEEE.
Schieritz, N. (2003). Emergent structures in supply chains - A study integrating agent-based and system dynamics modeling. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003.
Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management science, 35(3):321–339.
Sterman, J. D. (1992). Teaching Takes Off: Flight Simulators for Management Education. OR/MS Today, pages 40–44.
Swaminathan, J. M., Smith, S. F., and Sadeh, N. M. (1998). Modeling supply chain dynamics: A multiagent approach. Decision Sciences, 29(3):607–631.
Tversky, A. and Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157):1124–1131.
Vlachos, I. P. and Bourlakis, M. (2006). Supply chain collaboration between retailers and manufacturers: do they trust each other? In Supply Chain Forum: An International Journal, volume 7, pages 70–80. Taylor & Francis.
Wooldridge, M. J. (2000). Reasoning about rational agents. MIT press.
Collier, N. (2003). RePast : An Extensible Framework for Agent Simulation. The University of Chicagos Social Science Research, 36:371–375.
Croom, S., Romano, P., and Giannakis, M. (2000). Supply chain management: an analytical framework for critical literature review. European journal of purchasing & supply management, 6(1):67–83.
De La Fuente, D. and Lozano, J. (2007). Application of distributed intelligence to reduce the bullwhip effect. International Journal of Production Research, 45(8):1815–1833.
Eclipse, I. (2007). Eclipse foundation.
Edali, M. and Yasarcanb, H. (2014). A Mathematical Model of the Beer Game. JASSS.
Forrester, J. W. (1997). Industrial dynamics. Journal of the Operational Research Society, 48(10):1037–1041.
Friedman, M. (2007). The social responsibility of business is to increase its profits. Corporate ethics and corporate governance, pages 173–178.
Handfield, R. B. and Bechtel, C. (2002). The role of trust and relationship structure in improving supply chain responsiveness. Industrial marketing management, 31(4):367–382.
Hou, Y., Xiong, Y., Wang, X., and Liang, X. (2014). The effects of a trust mechanism on a dynamic supply chain network. Expert Systems with Applications, 41(6):3060–3068.
Kim, W. S. (2009). Effects of a trust mechanism on complex adaptive supply networks: An agent-based social simulation study. JASSS, 12(3).
Lambert, D. M. and Cooper, M. C. (2000). Issues in supply chain management. Industrial marketing management, 29(1):65–83.
Lee, H. L., Padmanabhan, V., and Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect. Source: Management Science, 43(4):546–558.
Lin, F.-r., Sung, Y.-W., and Lo, Y.-P. (2005). Effects of trust mechanisms on supply-chain performance: A multi-agent simulation study. International Journal of Electronic Commerce, 9(4):9–112.
Mayer, R. C., Davis, J. H., and Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3):709–734.
Nagurney, A. (2006). Supply chain network economics: dynamics of prices, flows and profits. Edward Elgar Publishing.
Ozik, J., Collier, N. T., Murphy, J. T., and North, M. J. (2013). The relogo agent-based modeling language. In Simulation Conference (WSC), 2013 Winter, pages 1560–1568. IEEE.
Schieritz, N. (2003). Emergent structures in supply chains - A study integrating agent-based and system dynamics modeling. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003.
Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management science, 35(3):321–339.
Sterman, J. D. (1992). Teaching Takes Off: Flight Simulators for Management Education. OR/MS Today, pages 40–44.
Swaminathan, J. M., Smith, S. F., and Sadeh, N. M. (1998). Modeling supply chain dynamics: A multiagent approach. Decision Sciences, 29(3):607–631.
Tversky, A. and Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157):1124–1131.
Vlachos, I. P. and Bourlakis, M. (2006). Supply chain collaboration between retailers and manufacturers: do they trust each other? In Supply Chain Forum: An International Journal, volume 7, pages 70–80. Taylor & Francis.
Wooldridge, M. J. (2000). Reasoning about rational agents. MIT press.
Publicado
02/05/2018
Como Citar
JALBUT, André Domingues da Silva; SICHMAN, Jaime Simão.
Impacto da Confiança em Simulação Baseada em Agentes para Cadeias de Suprimentos. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 12. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 73-84.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2018.33256.
