Genetic Algorithm based technique for solving Chance Constrained Problems |
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Authors: | Chandra A Poojari Boby Varghese |
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Institution: | Centre for the Analysis of Risk and Optimisation Modelling Applications (CARISMA), School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, United Kingdom |
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Abstract: | 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 efficiently represent and obtain good solutions for seven test problems. |
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Keywords: | Chance Constrained models Genetic Algorithm Simulation Stochastic Programming |
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