Stochastic programming model based on synthesizing effect function

In this paper, based on the stochastic optimization theory, we add synthesizing effect constraint condition to stochastic programming model. Through synthesizing effect constraint of expectation and variance, we establish a kind of stochastic programming model which has guiding significance. We also...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 2675 - 2679
Main Authors Lei Zhou, Fa-Chao Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Chance constrained programming
Constraint optimization
Constraint theory
Convex programming
Cybernetics
Dynamic programming
Exponential distribution
Functional programming
Gaussian distribution
Machine learning
Random variables
Stochastic processes
Stochastic programming
Synthesizing effect function
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Summary:In this paper, based on the stochastic optimization theory, we add synthesizing effect constraint condition to stochastic programming model. Through synthesizing effect constraint of expectation and variance, we establish a kind of stochastic programming model which has guiding significance. We also show that under certain condition, this new model is a convex programming, and prove that if the independent random variables are the normal distribution, the stochastic programming model with synthesizing effect function constraints is equivalent to chance constrained programming model. Last, we get the algorithm of this model.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212124