Stochastic simulation-based genetic algorithm for chance constraint programming problems with continuous random variables

Abstract

In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the random variables involved in the parameters follow any continuous distribution. Generally, deriving the deterministic equivalent of a chance constraint is very difficult due to complicated multivariate integration and is only possible if the random variables involved in the chance constraint follow some specific distribution such as normal, uniform, exponential and lognormal distribution. In the proposed method, the stochastic model is directly used. The feasibility of the chance constraints are checked using stochastic simulation, and the genetic algorithm is used to obtain the optimal solution. A numerical example is presented to prove the efficiency of the proposed method.

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Keywords:

• Stochastic programming
• Chance constraint
• Continuous random variable
• Stochastic simulation
• Genetic algorithm

Acknowledgement

This research is supported by Council of Scientific and Industrial Research (CSIR), Grant No. 9/81(416)/2003-EMR-1, New Delhi, India.

Notes

E-mail: [email protected]