The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability
International Journal of Economics, Finance and Management Sciences
Volume 1, Issue 6, December 2013, Pages: 318-322
Received: Oct. 8, 2013;
Published: Nov. 10, 2013
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Lei Wen, Department of Economics and Management, North China Electric Power University, Baoding 071003, China
Mingfang Guo, Department of Economics and Management, North China Electric Power University, Baoding 071003, China
Yachao Shi, Department of Economics and Management, North China Electric Power University, Baoding 071003, China
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Understanding supply chain network are important for modeling the spread of risks in enterprise nodes. This study characterizes the supply chain risk network of the spread of several nodes. To identify the rule of the movement of risk nodes, several parameters describing these properties are measured (degree, risk, the number of risk nodes, average risk, average path length and average clustering). The simulation results indicate: (1) this risk network has small-world and scale-free property; (2) the basic topological characteristics on static network displayed a regular change; (3) the characteristics of the spread of risk is measured by risk distribution which obeys a double power law and average risk which has a negative correlation with the number of risk node. In summation, this paper tries to analyze the risk spread of several nodes in supply chain network from macroscopic perspective.
Supply Chain, Complex Network, Risk Spread, Fixed Probability, Degree Distribution, Risk Distribution, Average Path Length, Average Clustering
To cite this article
The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability, International Journal of Economics, Finance and Management Sciences.
Vol. 1, No. 6,
2013, pp. 318-322.
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations[J]. Neuroimage, 2010, 52(3): 1059-1069.
Cohen R,ErezK, ben-Avraham D, et a.l Resilience of the internet to random breakdowns [J]. PhysRevLett, 2000,85(21): 4626-4628.
Bollobas B, Riordan O. Robustness and vulnerability of scale-free random graphs[J]. InternetMath, 2003, 1: 1-35.
A.E.Motter. Cascade Control and defense in Complex Networks[J].Phys Rev Lett，2004，93: 098701.
Christopher M, Peck H. Building the resilient supply chain[J]. International Journal of Logistics Management, The, 2004, 15(2): 1-14.
Klibi W, Martel A, Guitouni A. The design of robust value-creating supply chain networks: a critical review[J]. European Journal of Operational Research, 2010, 203(2): 283-293.
Vahdani B, Tavakkoli-Moghaddam R, Modarres M, et al. Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model[J]. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(6): 1152-1168.
Longinidis P, Georgiadis M C. Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty[J]. Computers & Chemical Engineering, 2012.
Klibi W, Martel A. Scenario-based supply chain network risk modeling[J]. European Journal of Operational Research, 2012.
Tang C S. Robust strategies for mitigating supply chain disruptions[J]. International Journal of Logistics: Research and Applications, 2006, 9(1): 33-45.
Pishvaee M S, Rabbani M, Torabi S A. A robust optimization approach to closed-loop supply chain network design under uncertainty[J]. Applied Mathematical Modelling, 2011, 35(2): 637-649.
Hosseini S, Dullaert W. Robust Optimization of Uncertain Logistics Networks[J]. Logistics Operations and Management: Concepts and Models, 2011: 359.
Friesz T L, Lee I, Lin C C. Competition and disruption in a dynamic urban supply chain[J]. Transportation Research Part B: Methodological, 2011, 45(8): 1212-1231.
Schönlein M, Makuschewitz T, Wirth F, et al. Measurement and optimization of robust stability of multiclass queueing networks: Applications in dynamic supply chains[J]. European Journal of Operational Research, 2013.