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SPEPS Preprint

Author(s): Chandra A. Poojari, Brunel University
Boby Varghese, Brunel University
Title: Genetic algorithm based technique for solving chance constrained problems
Date of Acceptance: 20.03.2006
Submission Date: 08.04.2005
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Submitted in: European Journal of Operations Research
Elsevier
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Abstract (ger):
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 are non-trivial to represent using algebraic modelling languages and pose significant computational challenges due to their non-linear, non-convex, and the stochastic nature. 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 the CCP problems are processed using Genetic Algorithm, whereas the stochastic nature is addressed through simulation. The computational investigations have shown that the framework can effciently represent and process a wide variety of the CCP problems.
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