DocumentCode :
3143130
Title :
MCS-PSO based risk programming for virtual enterprise
Author :
Lu, Fuqiang ; Wu, Zhongyuan ; Wu, Cuihua
Author_Institution :
Coll. of Manage., Tianjin Polytech. Univ., Tianjin, China
Volume :
7
fYear :
2010
fDate :
16-18 Oct. 2010
Firstpage :
2909
Lastpage :
2912
Abstract :
This paper designes a Monte Carlo Simulation combined Particle Swarm Optimization (MCS-PSO) for the stochastic risk programming model of virtual enterprise (VE). The stochastic characters of the risk in VE are considered, which are described by random variables. So a stochastic programming model is proposed for risk management of VE. In detail, this is a chance constraint programming model, One of the great advantages of this class of model is that it can actually describe the risk preference of the manager. When the number of risk factors and the number of actions increase, the size of the problem will be huge. Therefore Particle Swarm Optimization (PSO) is employed to sovle the problem. On the other hand, to deal with the random variables, Monte Carlo Simulation is combined with PSO (MCS-PSO). Finally, numerical examples are given to illustrate the effectiveness of the MCS-PSO and the result shows that the risk programming model is very useful for VE.
Keywords :
Monte Carlo methods; constraint handling; particle swarm optimisation; random functions; risk analysis; stochastic programming; virtual enterprises; Monte Carlo simulation; constraint programming; particle swarm optimization; random variable; stochastic risk programming; virtual enterprise; Monte Carlo methods; Numerical models; Programming profession; Random variables; Stochastic processes; Virtual enterprises; monte carlo simulation; particle swarm optimization; risk programming; virtual enterprise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6495-1
Type :
conf
DOI :
10.1109/BMEI.2010.5639581
Filename :
5639581
Link To Document :
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