JACIII Vol.20 No.4 pp. 623-632
doi: 10.20965/jaciii.2016.p0623


Influence of the Chinese Government Subsidy Policies on Supply Chain Members’ Profits: An Agent-Based Modeling and Simulation Approach

Ran Zhang*,** and Jie Lin*

*School of Economic and Management, Tongji University
Shanghai 200092, China

**School of Statistics and Information, Xinjiang University of Finance and Economics
Urumqi 830012, China

December 9, 2015
May 2, 2016
Online released:
July 19, 2016
July 19, 2016
multi-agent, government subsidy, subsidy limit, supply chain simulation, profit distribution

The series of subsidy policies launched by the Chinese government has affected supply chain members’ profits distribution. To explore this influence, an agent-based model was designed, and experiments were conducted under different subsidy levels. Our model focused on the ordinary business entities and their activities in the supply chain. By investigating the real world and other researchers’ studies, agent simulation class library (e.g., control agents, cooperation/collaboration agents, and fractal simulation agents) and their decision knowledge bases were designed to simulate the supply chain members’ behaviors, decision processes, and operation and production activities and behaviors. Price model, demand model and profit model under the subsidy were built to evaluate the supply chain members’ profits under different subsidy scenarios. Finally, a multi-echelon appliance supply chain model was constructed, and experiments were performed with different levels of subsidy limit. Results showed that the supply chain members’ profits increased under the government subsidy policy. The agent-based modeling and simulation method provides a novel approach to explore the impact on profit distribution.

  1. [1] Y. W. Chao and H. Yin, “The impact of financial subsidies on Rural Residents Consumption,” Journal of Agrotechnical Economics, Vol.03, pp. 61-70, 2015.
  2. [2] Y. H. Zhou and B. Ren, “Pricing, Xiaxiang and Sales: Empirical analysis from motorcycle industry,” Industrial Economical Review, Vol.02, pp. 87-95, 2015.
  3. [3] Editorial Department, “’Home appliances to the countryside’ course review,” Electrical Appliances, Vol.02, pp. 10-12, 2015.
  4. [4] C. M. Xu, “Analysis on implementations effect of home appliances going to the countryside,” J. of Anhui Agriculture Science, Vol.41, No.5, pp. 2346-2348, 2013.
  5. [5] H. G. Tian, “Influence of the policy of household appliances to the countryside on the consumption of rural residents,” J. of Anhui Agriculture Science, Vol.39, No.13, pp. 8127-8130, 2011.
  6. [6] X. X. Wu and W. Q. Xiong, “Evolutionary game analysis of reverse supply chain based on government subsidy mechanism,” J. of Green Science and Technology, Vol.8, pp. 191-195, 2012.
  7. [7] G. B. Qiu, “Influence on decisions of manufacturer and retailer by different modes of government’s subsidy,” Scientific Decision Making, Vol.7, pp. 12-24, 2013.
  8. [8] F. Campuzano and J. Mula, “Supply Chain Simulation,” Springer, 2011.
  9. [9] Fu-ren Lin, Hui-chun Kuo, and Shyh-ming Lin, “The enhancement of solving the distributed constraint satisfaction problem for cooperative supply chains using multi-agent systems,” Decision Support Systems, Vol.45, pp. 795-810, 2008.
  10. [10] M. Wang, H. Wang, D. Vogel, K. Kumar, and D. K. W. Chiu, “Agent-based negotiation and decision making for dynamic supply chain formation,” Engineering Applications of Artificial Intelligence, Vol.22, pp. 1046-1055, 2009.
  11. [11] M. Wooldridge and N. R. Jennings, “Agent theories, architectures, and languages: a survey,” Intelligent agents: Springer, pp. 1-39, 1995.
  12. [12] W. Y. Liang and C. C. Huang, “Agent-based demand forecast in multi-echelon supply chain,” Decision Support Systems, Vol.42, No.1, pp. 390-407, 2006.
  13. [13] P. Renna and P. Argoneto, “A game theoretic coordination for trading capacity in multisite factory environment,” Int. J. of Advanced Manufacturing Technology, Vol.47, pp. 1241-1252, 2010.
  14. [14] K. Zaima, “Conditions to Diffuse Green Management into SMEs and the Role of Knowledge Support: Agent-Based Modeling,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, pp. 252-262, 2013.
  15. [15] K. Ryu and M. Jung, “Agent-based fractal architecture and modelling for developing distributed manufacturing systems,” Int. J. of Production Research, Vol.41, No.17, pp. 4233-4255, 2003.
  16. [16] X. J. Fan and H. M. Chen, “Research on the self-organization model of fractal supply chain,” Chinese J. of Management Science, Vol.16, No.6, pp. 61-66, 2008.
  17. [17] Y. Yang, G. L. Lin and Z. H. Hu, “Research on service supply chain networks organization based on fractal theory-Taking port service supply chain as an example,” West Forum, Vol.22, No.2, pp. 59-65, 2012.
  18. [18] M. Guo, “A concise course of inventory theory,” Huazhong University of Science & Technology Press, 2013.
  19. [19] R. Q. Chen and S. H. Ma, “Production and operations management,” China Machine Press, 2009.
  20. [20] S. R. Tang, “Analysis of relationship between supplier and manufacturer based on game theory,” Ocean University of China, 2008.
  21. [21] D. Y. Mou, F. S. Tu, and Q. S. Chen, “Co-op pricing decision model for a manufacturing-retailing supply chain,” Acta Scientiarum Naturalium Universitatis Nankaiensis, Vol.37, pp. 55-60, 2004.
  22. [22] J. Liu and G. B. Qiu, “Research on the game between manufacturers and retailers in the background of government’s subsidy,” Soft Science, Vol.25, No.9, pp. 48-53, 2011.
  23. [23] L. Zhou, “Research on subsidy model and subsidy object of the ’home appliances going to the countryside’,” University of Electronic Science and Technology of China, 2010.
  24. [24] J. Lin and K. Cao, “Profit distribution of members in closed-loop supply chain with government replacement-subsidy,” J. of Tongji University (natural Science), Vol.42, pp. 0651-0658, 2014.
  25. [25] J. C. Hennet and Y. Arda, “Supply chain coordination: A game-theory approach,” Engineering Applications of Artificial Intelligence, Vol.21, pp. 399-405, 2008.
  26. [26] Z. Zhong, “Research on evaluation method for credibility of simulation system,” Harbin Institute of Technology, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 28, 2017