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

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 feasib...

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Bibliographic Details
Published in:International journal of computer mathematics Vol. 81; no. 12; pp. 1455 - 1463
Main Authors: Jana, R. K., Biswal, M. P.
Format: Journal Article
Language:English
Published: Taylor & Francis 01.12.2004
Subjects:
Chance constraint
Discrete random variables
Genetic algorithm
Stochastic programming
Stochastic simulation
ISSN:0020-7160, 1029-0265
Online Access:Get full text
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Summary: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
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ISSN:0020-7160
1029-0265
DOI:10.1080/0020716042000272584