Computer Science ›› 2020, Vol. 47 ›› Issue (7): 171-178.doi: 10.11896/jsjkx.190600038

• Artificial Intelligence • Previous Articles     Next Articles

Evolution Analysis of Household Car Supply Chain Based on Multi-Agent

SUN Jun-yan, ZHANG Yuan-yuan, WU Bing-ying, NIU Ya-ru, CHEN Chan-juan   

  1. College of Mechanical and Electrical Engineering,Shaanxi University of Science & Technology,Xi’an 710021,China
  • Received:2019-06-11 Online:2020-07-15 Published:2020-07-16
  • About author:SUN Jun-yan,born in 1978,Ph.D,associate professor,postgraduate supervisor.Her main research interests include logistics information technology,and supply chain management.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (51275407,51475363,11072192), Key Science and Technology Program of Shaanxi Province, China (2018GY-026),Shaanxi University of Science and Technology Ph.D. Research Startup Fund (2018BJ-12) and National College Students Innovation and Entrepreneurship Training Program (12145)

Abstract: In order to explore the business strategy of multi-model product supply chain,this paper used Multi-Agent modeling and simulation method to establish a multi-model home vehicle supply chain network model.Aiming at maximizing the profit of manufacturer Agent,stimulus-response learning mechanism and the particle swarm learning mechanism are used to analyze the evolution of the model.The simulations show that,firstly,by using the stimulus-response learning mechanism,the sales volume of some models of the manufacturer will be greatly reduced.For manufacturers pursuing multiple models,it is not conducive to the promotion of multiple models,but the total sales volume and profits will increase.Secondly,by using the particle swarm learning mechanism,no matter what combination of C1 and C2 is,it is difficult to obtain the optimal sales and profit at the same time.For mass-consumer models,manufacturers can choose to strengthen C2 and expand the market with “small profits but quick turnover” strategy to increase sales.For high-end consumer models,manufacturers can choose to strengthen C1 and create high-quality products with a “benefit and profitable” strategy to increase profits.Relatively speaking,by using particle swarm learning mechanisms,manufacturers can quickly adjust strategies to cope with market changes,and the strategies are more stable after learning.This study has practical guidance for supply chain management with multiple models.

Key words: Supply chain, Multi-vehicle, Stimulus-response theory, Particle swarm optimization, Evolution analysis

CLC Number: 

  • TP311
[1] KAMALENDU P,BILL K.A multi agent-based service framework for supply chain management [J].Procedia Computer Science,2014,32:53-60.
[2] BOTTANI E,MURINO T,SCHIAVO M,et al.Resilient food supply chain design:Modelling framework and metaheuristic solution approach[J].Computers & Industrial Engineering,2019,135:177-198.
[3] SUN J Y,TANG J M,FU W P,et al.Hybrid modeling and empirical analysis of automobile supply chain network[J].Physica A:Statistical Mechanics and its Applications,2017,473:377-389.
[4] BARRAT A,BARTHÉLEMY M,VESPIGNANI A.Weighted evolving networks:coupling topology and weight dynamics [J].Physical Review Letters,2004,92(22):228701.
[5] ORENSTEIN P.How does supply network evolution and its topological structure impact supply chain performance[C]//Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering,Life Science.2016:562-569.
[6] LIAO Z D,ZHENG G H.Research on the evolution law of supply chain considering the characteristics of different nodes’ behavioral elements [J].Computer Application Research,2020,37(6):18-25.
[7] YANG Q F,WANG Y Y,REN Y D.Research on financial risk management model of internet supply chain based on data science[J].Cognitive Systems Research,2019,56:50-55.
[8] SUN J Y,FU W P,WANG W,et al.Modelling and simulation of the supply chain of automobile industry[J].International Journal of Simulation:System,Science and Technology,2016,13:22-29.
[9] ZHAO Z G,ZHOU G G,PAN R F.Research on localized world evolution model of weighted complex supply chain network based on location attractiveness[J].Computer Science,2018,45(12):71-76.
[10] FAHHAMA L,ZAMMA A,MANSOURI K,et al.Towards a mixed method model and simulation of the automotive supply chain network connectivity[J].International Colloquium on Logistics and Supply Chain Management,2017,7:173-186.
[11] GUO C,LIU X,JIN M,et al.The research on optimization of auto supply chain network robust model under macroeconomic fluctuations[J].Chaos Solitons & Fractals the Interdisciplinary Journal of Nonlinear Science & Nonequilibrium & Complex Phenomena,2016,89(3):105-114.
[12] LANGROODI R R P,AMIRI M.A system dynamics modeling approach for a multi-level,multi-product,multi-region supply chain under demand uncertainty[J].Expert Systems with Applications,2016,51:231-244.
[13] CIGOLINI R,PERO M,ROSSI T,et al.Linking supply chain configuration to supply chain perfrmance:A discrete event simu-lation model[J].Simulation Modelling Practice & Theory,2014,40:1-11.
[14] DORUK R O,ZHANG K.Fitting of dynamic recurrent neural network models to sensory stimulus-response data[J].Turkish Journal of Electrical Engineering and Computer Sciences,2017,27:903-920.
[15] ZHAO Q,WANG W,CHEN H,et al.Pareto improvements for a supply chain with price-only contracts based on quick response[J].International Conference on Service Systems and Service Management,2015(4):1-5.
[16] BASHIRI M,REZAEI H R.Reconfiguration of Supply Chain:A Two Stage Stochastic Programming[J].International Journal of Industrial Engineering & Production Res,2013,24:47-50.
[17] ZHAO J,LIU J,MEI S E.Optimization Strategy of Enterprise Targeted Advertising Based on Mixed Channels[J].Computer Integrated Manufacturing Systems,2019(5):1-13.
[18] HWANG S N.Upcoming tipping points in automobile industry based on agents-based modeling[J].Procedia Computer Science,2012(8):93-99.
[19] BYUNG-DOKIM,BLATTBERG R,ROSSI P.Modeling theDistribution of Price Sensitivity and Implications for Optimal Retail Pricing[J].Journal of Business & Economic Statistics,1995,13(3):291-303.
[20] MORADINASAB N,AMIN-NASERI M R,BEHBAHANI T J,et al.Competition and cooperation between supply chains in multi-objective petroleum green supply chain:A game theoretic approach[J].Journal of Cleaner Production,2018,170:818-841.
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