2006-03-20Buch DOI: 10.18452/8352
Genetic algorithm based technique for solving chance constrained problems
Management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance Constrained Programming (CCP) have been used for modelling and analysis of risks in a number of application domains. However, the resulting mathematical problems arenon-trivial to represent using algebraic modelling languages and pose signiﬁcantcomputational challenges due to their non-linear, non-convex, and the stochasticnature. We develop and implement C++ classes to represent such CCP problems.We propose a framework consisting of Genetic Algorithm and Monte-Carlo simulation in order to process the problems. The non-linear and non-convex nature of theCCP problems are processed using Genetic Algorithm, whereas the stochastic nature is addressed through simulation. The computational investigations have shownthat the framework can effciently represent and process a wide variety of the CCPproblems.
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