Stochastic simulation based genetic algorithm for chance constraint programming problems with some discrete random variables

Authors: R. K. Jana; M. P. Biswal

Source: International Journal of Computer Mathematics, Volume 81, Number 12, December 2004 , pp. 1455-1463(9)

Publisher: Taylor and Francis Ltd

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Abstract:

A stochastic simulation based genetic algorithm (GA) is presented, in this paper, for solving chance constraint programming problems in which the random variables follow some discrete distributions. The feasibility of the chance constraints is checked by stochastic simulation. In general, the feasible region associate with such problems is non-convex. Therefore, GA is used to obtain the optimal solution. In the proposed method, the stochastic model is directly used without finding the deterministic equivalent of it. A numerical example is presented to prove the efficiency of the proposed method.*

E-mail: rabin@maths.iitkgp.ernet.in

Keywords: Stochastic Programming; Chance Constraint; Discrete Random Variables; Stochastic Simulation; Genetic Algorithm

Document Type: Research article

DOI: 10.1080/0020716042000272584

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